Two pairs of enantiomers,(−)and(+)-securidanes A(1 and 2)and B(3 and 4)featuring unprecedented triarylmethane(TAM)skeletons,were isolated from Securidaca inappendiculata.Teir structures were established by spectroscop...Two pairs of enantiomers,(−)and(+)-securidanes A(1 and 2)and B(3 and 4)featuring unprecedented triarylmethane(TAM)skeletons,were isolated from Securidaca inappendiculata.Teir structures were established by spectroscopic data,X-ray crystallography,and CD analysis.A plausible biosynthetic pathway for 1−4 based on the co-isolated precursors was proposed.Bioinspired total synthesis of 1−4was completed in high yield,which in turn corroborated the biosynthetic hypothesis.Compounds 1−4 showed good inhibition against protein tyrosine phosphatase 1B(PTP1B).Te molecular docking demonstrated that the strongest inhibitor 3(IC50=7.52�M)reaches deeper into the binding pocket and has an additional H-bond.展开更多
Fecal microbiota transplantation(FMT)has shown promising results in animal models of obesity,while results in human studies are inconsistent.We aimed to determine factors associated with weight loss after FMT in nine ...Fecal microbiota transplantation(FMT)has shown promising results in animal models of obesity,while results in human studies are inconsistent.We aimed to determine factors associated with weight loss after FMT in nine obese subjects using serial multi-omics analysis of the fecal and mucosal microbiome.The mucosal microbiome,fecal microbiome,and fecal metabolome showed individual clustering in each subject after FMT.The colonic microbiome in patients showed more marked variance after FMT compared with the duodenal microbiome,characterized by an increased relative abundance of Bacteroides.Subjects who lost weight after FMT sustained enrichment of Bifidobacterium bifidum and Alistipes onderdonkii in the duodenal,colonic mucosal,and fecal microbiome and increased levels of phosphopantothenate biosynthesis and fecal metabolite eicosapentaenoic acid(EPA),compared with those without weight loss.Fecal levels of amino acid metabolism-associated were positively correlated with the fecal abundance of B.bifidum,and fatty acid metabolism-associated metabolites showed positive correlations with A.onderdonkii.展开更多
Background:A One Health approach has been increasingly mainstreamed by the international community, as it provides for holistic thinking in recognizing the close links and inter-dependence of the health of humans, ani...Background:A One Health approach has been increasingly mainstreamed by the international community, as it provides for holistic thinking in recognizing the close links and inter-dependence of the health of humans, animals and the environment. However, the dearth of real-world evidence has hampered application of a One Health approach in shaping policies and practice. This study proposes the development of a potential evaluation tool for One Health performance, in order to contribute to the scientific measurement of One Health approach and the identification of gaps where One Health capacity building is most urgently needed.Methods:We describe five steps towards a global One Health index (GOHI), including (i) framework formulation;(ii) indicator selection;(iii) database building;(iv) weight determination;and (v) GOHI scores calculation. A cell-like framework for GOHI is proposed, which comprises an external drivers index (EDI), an intrinsic drivers index (IDI) and a core drivers index (CDI). We construct the indicator scheme for GOHI based on this framework after multiple rounds of panel discussions with our expert advisory committee. A fuzzy analytical hierarchy process is adopted to determine the weights for each of the indicators.Results:The weighted indicator scheme of GOHI comprises three first-level indicators, 13 second-level indicators, and 57 third-level indicators. According to the pilot analysis based on the data from more than 200 countries/territories the GOHI scores overall are far from ideal (the highest score of 65.0 out of a maximum score of 100), and we found considerable variations among different countries/territories (31.8–65.0). The results from the pilot analysis are consistent with the results from a literature review, which suggests that a GOHI as a potential tool for the assessment of One Health performance might be feasible.Conclusions:GOHI—subject to rigorous validation—would represent the world’s first evaluation tool that constructs the conceptual framework from a holistic per展开更多
We examine the role of big data and machine learning in cancer research.We describe an example in cancer research where gene-level data from The Cancer Genome Atlas(TCGA) consortium is interpreted using a pathway-leve...We examine the role of big data and machine learning in cancer research.We describe an example in cancer research where gene-level data from The Cancer Genome Atlas(TCGA) consortium is interpreted using a pathway-level model.As the complexity of computational models increases,their sample requirements grow exponentially.This growth stems from the fact that the number of combinations of variables grows exponentially as the number of variables increases.Thus,a large sample size is needed.The number of variables in a computational model can be reduced by incorporating biological knowledge.One particularly successful way of doing this is by using available gene regulatory,signaling,metabolic,or context-specific pathway information.We conclude that the incorporation of existing biological knowledge is essential for the progress in using big data for cancer research.展开更多
Identification of genetic signatures is the main objective for many computational oncology studies. The signature usually consists of numerous genes that are differentially expressed between two clinically distinct gr...Identification of genetic signatures is the main objective for many computational oncology studies. The signature usually consists of numerous genes that are differentially expressed between two clinically distinct groups of samples, such as tumor subtypes. Prospectively, many signatures have been found to generalize poorly to other datasets and, thus, have rarely been accepted into clinical use. Recognizing the limited success of traditionally generated signatures, we developed a systems biology-based framework for robust identification of key transcription factors and their genomic regulatory neighborhoods. Application of the framework to study the differences between gastrointestinal stromal tumor (GIST) and leiomyosarcoma (LMS) resulted in the identification of nine transcription factors (SRF, NKX2-5, CCDC6, LEF1, VDR, ZNF250, TRIM63, MAF, and MYC). Functional annotations of the obtained neighborhoods identified the biological processes which the key transcription factors regulate differently between the tumor types. Analyzing the differences in the expression patterns using our approach resulted in a more robust genetic signature and more biological insight into the diseases compared to a traditional genetic signature.展开更多
In this editorial we introduce the research paradigms of signal processing in the era of systems biology.Signal processing is a field of science traditionally focused on modeling electronic and communications systems,...In this editorial we introduce the research paradigms of signal processing in the era of systems biology.Signal processing is a field of science traditionally focused on modeling electronic and communications systems,but recently it has turned to biological applications with astounding results.The essence of signal processing is to describe the natural world by mathematical models and then,based on these models,develop efficient computational tools for solving engineering problems.Here,we underline,with examples,the endless possibilities which arise when the battle-hardened tools of engineering are applied to solve the problems that have tormented cancer researchers.Based on this approach,a new field has emerged,called cancer systems biology.Despite its short history,cancer systems biology has already produced several success stories tackling previously impracticable problems.Perhaps most importantly,it has been accepted as an integral part of the major endeavors of cancer research,such as analyzing the genomic and epigenomic data produced by The Cancer Genome Atlas(TCGA) project.Finally,we show that signal processing and cancer research,two fields that are seemingly distant from each other,have merged into a field that is indeed more than the sum of its parts.展开更多
基金Te authors thank Professor S.Q.Tang of Guangxi Normal University for the identifcation of the plant material.Te National Natural Science Foundation(nos.21532007,U1302222,81321092)and the Foundation from the MOST(2012CB721105)of China are gratefully acknowledged.
文摘Two pairs of enantiomers,(−)and(+)-securidanes A(1 and 2)and B(3 and 4)featuring unprecedented triarylmethane(TAM)skeletons,were isolated from Securidaca inappendiculata.Teir structures were established by spectroscopic data,X-ray crystallography,and CD analysis.A plausible biosynthetic pathway for 1−4 based on the co-isolated precursors was proposed.Bioinspired total synthesis of 1−4was completed in high yield,which in turn corroborated the biosynthetic hypothesis.Compounds 1−4 showed good inhibition against protein tyrosine phosphatase 1B(PTP1B).Te molecular docking demonstrated that the strongest inhibitor 3(IC50=7.52�M)reaches deeper into the binding pocket and has an additional H-bond.
文摘Fecal microbiota transplantation(FMT)has shown promising results in animal models of obesity,while results in human studies are inconsistent.We aimed to determine factors associated with weight loss after FMT in nine obese subjects using serial multi-omics analysis of the fecal and mucosal microbiome.The mucosal microbiome,fecal microbiome,and fecal metabolome showed individual clustering in each subject after FMT.The colonic microbiome in patients showed more marked variance after FMT compared with the duodenal microbiome,characterized by an increased relative abundance of Bacteroides.Subjects who lost weight after FMT sustained enrichment of Bifidobacterium bifidum and Alistipes onderdonkii in the duodenal,colonic mucosal,and fecal microbiome and increased levels of phosphopantothenate biosynthesis and fecal metabolite eicosapentaenoic acid(EPA),compared with those without weight loss.Fecal levels of amino acid metabolism-associated were positively correlated with the fecal abundance of B.bifidum,and fatty acid metabolism-associated metabolites showed positive correlations with A.onderdonkii.
基金The project was supported by China Medical Board(no.20-365)Shanghai Jiao Tong University Integrated Innovation Fund(no.2020-01).
文摘Background:A One Health approach has been increasingly mainstreamed by the international community, as it provides for holistic thinking in recognizing the close links and inter-dependence of the health of humans, animals and the environment. However, the dearth of real-world evidence has hampered application of a One Health approach in shaping policies and practice. This study proposes the development of a potential evaluation tool for One Health performance, in order to contribute to the scientific measurement of One Health approach and the identification of gaps where One Health capacity building is most urgently needed.Methods:We describe five steps towards a global One Health index (GOHI), including (i) framework formulation;(ii) indicator selection;(iii) database building;(iv) weight determination;and (v) GOHI scores calculation. A cell-like framework for GOHI is proposed, which comprises an external drivers index (EDI), an intrinsic drivers index (IDI) and a core drivers index (CDI). We construct the indicator scheme for GOHI based on this framework after multiple rounds of panel discussions with our expert advisory committee. A fuzzy analytical hierarchy process is adopted to determine the weights for each of the indicators.Results:The weighted indicator scheme of GOHI comprises three first-level indicators, 13 second-level indicators, and 57 third-level indicators. According to the pilot analysis based on the data from more than 200 countries/territories the GOHI scores overall are far from ideal (the highest score of 65.0 out of a maximum score of 100), and we found considerable variations among different countries/territories (31.8–65.0). The results from the pilot analysis are consistent with the results from a literature review, which suggests that a GOHI as a potential tool for the assessment of One Health performance might be feasible.Conclusions:GOHI—subject to rigorous validation—would represent the world’s first evaluation tool that constructs the conceptual framework from a holistic per
文摘We examine the role of big data and machine learning in cancer research.We describe an example in cancer research where gene-level data from The Cancer Genome Atlas(TCGA) consortium is interpreted using a pathway-level model.As the complexity of computational models increases,their sample requirements grow exponentially.This growth stems from the fact that the number of combinations of variables grows exponentially as the number of variables increases.Thus,a large sample size is needed.The number of variables in a computational model can be reduced by incorporating biological knowledge.One particularly successful way of doing this is by using available gene regulatory,signaling,metabolic,or context-specific pathway information.We conclude that the incorporation of existing biological knowledge is essential for the progress in using big data for cancer research.
基金supported by Project for the Biological Information and Information Processing Properties of Biological Systems from the Academy of Finland(No.122973)Project for the Structure-dynamics Relationships in Biological Network from the Academy of Finland(No.132877)Finnish Funding Agency for Technology and Innovation Finland Distinguished Professor program(No.1480/31/09)
文摘Identification of genetic signatures is the main objective for many computational oncology studies. The signature usually consists of numerous genes that are differentially expressed between two clinically distinct groups of samples, such as tumor subtypes. Prospectively, many signatures have been found to generalize poorly to other datasets and, thus, have rarely been accepted into clinical use. Recognizing the limited success of traditionally generated signatures, we developed a systems biology-based framework for robust identification of key transcription factors and their genomic regulatory neighborhoods. Application of the framework to study the differences between gastrointestinal stromal tumor (GIST) and leiomyosarcoma (LMS) resulted in the identification of nine transcription factors (SRF, NKX2-5, CCDC6, LEF1, VDR, ZNF250, TRIM63, MAF, and MYC). Functional annotations of the obtained neighborhoods identified the biological processes which the key transcription factors regulate differently between the tumor types. Analyzing the differences in the expression patterns using our approach resulted in a more robust genetic signature and more biological insight into the diseases compared to a traditional genetic signature.
文摘In this editorial we introduce the research paradigms of signal processing in the era of systems biology.Signal processing is a field of science traditionally focused on modeling electronic and communications systems,but recently it has turned to biological applications with astounding results.The essence of signal processing is to describe the natural world by mathematical models and then,based on these models,develop efficient computational tools for solving engineering problems.Here,we underline,with examples,the endless possibilities which arise when the battle-hardened tools of engineering are applied to solve the problems that have tormented cancer researchers.Based on this approach,a new field has emerged,called cancer systems biology.Despite its short history,cancer systems biology has already produced several success stories tackling previously impracticable problems.Perhaps most importantly,it has been accepted as an integral part of the major endeavors of cancer research,such as analyzing the genomic and epigenomic data produced by The Cancer Genome Atlas(TCGA) project.Finally,we show that signal processing and cancer research,two fields that are seemingly distant from each other,have merged into a field that is indeed more than the sum of its parts.
