Trimodal Therapy: A Tumor Vascularâ Targeted Interlocking Trimodal Nanosystem That Induces and Exploits Hypoxia (Adv. Sci. 8/2018)

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/145503/1/advs201870048.pd

Genome-Wide Transcriptome and Antioxidant Analyses on Gamma-Irradiated Phases of Deinococcus radiodurans R1

Adaptation of D. radiodurans cells to extreme irradiation environments requires dynamic interactions between gene expression and metabolic regulatory networks, but studies typically address only a single layer of regulation during the recovery period after irradiation. Dynamic transcriptome analysis of D. radiodurans cells using strand-specific RNA sequencing (ssRNA-seq), combined with LC-MS based metabolite analysis, allowed an estimate of the immediate expression pattern of genes and antioxidants in response to irradiation. Transcriptome dynamics were examined in cells by ssRNA-seq covering its predicted genes. Of the 144 non-coding RNAs that were annotated, 49 of these were transfer RNAs and 95 were putative novel antisense RNAs. Genes differentially expressed during irradiation and recovery included those involved in DNA repair, degradation of damaged proteins and tricarboxylic acid (TCA) cycle metabolism. The knockout mutant crtB (phytoene synthase gene) was unable to produce carotenoids, and exhibited a decreased survival rate after irradiation, suggesting a role for these pigments in radiation resistance. Network components identified in this study, including repair and metabolic genes and antioxidants, provided new insights into the complex mechanism of radiation resistance in D. radiodurans

A Tumor Vascularâ Targeted Interlocking Trimodal Nanosystem That Induces and Exploits Hypoxia

Vascularâ targeted photodynamic therapy (VTP) is a recently approved strategy for treating solid tumors. However, the exacerbated hypoxic stress makes tumor eradication challenging with such a single modality approach. Here, a new graphene oxide (GO)â based nanosystem for rationally designed, interlocking trimodal cancer therapy that enables VTP using photosensitizer verteporfin (VP) (1) with codelivery of banoxantrone dihydrochloride (AQ4N) (2), a hypoxiaâ activated prodrug (HAP), and HIFâ 1α siRNA (siHIFâ 1α) (3) is reported. The VTPâ induced aggravated hypoxia is highly favorable for AQ4N activation into AQ4 (a topoisomerase II inhibitor) for chemotherapy. However, the hypoxiaâ induced HIFâ 1α acts as a â hidden brake,â through downregulating CYP450 (the dominant HAPâ activating reductases), to substantially hinder AQ4N activation. siHIFâ 1α is rationally adopted to suppress the HIFâ 1α expression upon hypoxia and further enhance AQ4N activation. This trimodal nanosystem significantly delays the growth of PCâ 3 tumors in vivo compared to the control nanoparticles carrying VP, AQ4N, or siHIFâ 1α alone or their pairwise combinations. This multimodal nanoparticle design presents, the first example exploiting VTP to actively induce hypoxia for enhanced HAP activation. It is also revealed that HAP activation is still insufficient under hypoxia due to the hidden downregulation of the HAPâ activating reductases (CYP450), and this can be well overcome by GO nanoparticleâ mediated siHIFâ 1α intervention.Vascularâ targeted photodynamic therapy (VTP) is integrated with hypoxiaâ activated prodrug (AQ4N) and HIFâ 1α siRNA (siHIFâ 1α) for interlocking trimodal therapy. The VTPâ induced aggravated hypoxia is exploited for efficient AQ4N activation for chemotherapy. HIFâ 1α induced by hypoxia acts as a â hidden brake,â through downregulating CYP450 reductases, to hinder AQ4N activation. siHIFâ 1α is rationally adopted to suppress HIFâ 1α expression upon VTP to enhance AQ4N activation.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/145505/1/advs661-sup-0001-S1.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/145505/2/advs661.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/145505/3/advs661_am.pd

Encroachment order and spatial patterns of broad-leaf tree species in the naturalization of a Cunninghamia lanceolata plantation

Authors of the article state that the aim of this study was to understand the encroachment order, spatial patterns, interspecific associations, and species diversity of a Cunninghamia lanceolata plantation and to provide context for how to improve the spatial structure of C. lanceolata plantations. Their results show that the encroachment of broad-leaf trees into the C. lanceolata plantation followed a clear successional sequence of tree community assembly: intolerant tree species encroachment first, such as Alniphyllum fortunei and Liquidambar formosana, encroached; neutral tree species then encroachment, such as Daphniphyllum oldhamii and Schima superba, and shade-tolerant tree species encroachment last, such as Castanopsis eyrei and Castanopsis tibetana

Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis

Publisher Copyright: © 2022, The Author(s).Background: Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery. Results: To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3–5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism. Conclusions: Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk.Peer reviewe

Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis

Abstract Background Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery. Results To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3–5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism. Conclusions Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk

Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis

Funding GMP, PN, and CW are supported by NHLBI R01HL127564. GMP and PN are supported by R01HL142711. AG acknowledge support from the Wellcome Trust (201543/B/16/Z), European Union Seventh Framework Programme FP7/2007–2013 under grant agreement no. HEALTH-F2-2013–601456 (CVGenes@Target) & the TriPartite Immunometabolism Consortium [TrIC]-Novo Nordisk Foundation’s Grant number NNF15CC0018486. JMM is supported by American Diabetes Association Innovative and Clinical Translational Award 1–19-ICTS-068. SR was supported by the Academy of Finland Center of Excellence in Complex Disease Genetics (Grant No 312062), the Finnish Foundation for Cardiovascular Research, the Sigrid Juselius Foundation, and University of Helsinki HiLIFE Fellow and Grand Challenge grants. EW was supported by the Finnish innovation fund Sitra (EW) and Finska Läkaresällskapet. CNS was supported by American Heart Association Postdoctoral Fellowships 15POST24470131 and 17POST33650016. Charles N Rotimi is supported by Z01HG200362. Zhe Wang, Michael H Preuss, and Ruth JF Loos are supported by R01HL142302. NJT is a Wellcome Trust Investigator (202802/Z/16/Z), is the PI of the Avon Longitudinal Study of Parents and Children (MRC & WT 217065/Z/19/Z), is supported by the University of Bristol NIHR Biomedical Research Centre (BRC-1215–2001) and the MRC Integrative Epidemiology Unit (MC_UU_00011), and works within the CRUK Integrative Cancer Epidemiology Programme (C18281/A19169). Ruth E Mitchell is a member of the MRC Integrative Epidemiology Unit at the University of Bristol funded by the MRC (MC_UU_00011/1). Simon Haworth is supported by the UK National Institute for Health Research Academic Clinical Fellowship. Paul S. de Vries was supported by American Heart Association grant number 18CDA34110116. Julia Ramierz acknowledges support by the People Programme of the European Union’s Seventh Framework Programme grant n° 608765 and Marie Sklodowska-Curie grant n° 786833. Maria Sabater-Lleal is supported by a Miguel Servet contract from the ISCIII Spanish Health Institute (CP17/00142) and co-financed by the European Social Fund. Jian Yang is funded by the Westlake Education Foundation. Olga Giannakopoulou has received funding from the British Heart Foundation (BHF) (FS/14/66/3129). CHARGE Consortium cohorts were supported by R01HL105756. Study-specific acknowledgements are available in the Additional file 32: Supplementary Note. The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute; the National Institutes of Health; or the U.S. Department of Health and Human Services.Peer reviewedPublisher PD

Genetic Drivers of Heterogeneity in Type 2 Diabetes Pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P \u3c 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care

Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P &lt; 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.</p

The trans-ancestral genomic architecture of glycemic traits

Glycemic traits are used to diagnose and monitor type 2 diabetes and cardiometabolic health. To date, most genetic studies of glycemic traits have focused on individuals of European ancestry. Here we aggregated genome-wide association studies comprising up to 281,416 individuals without diabetes (30% non-European ancestry) for whom fasting glucose, 2-h glucose after an oral glucose challenge, glycated hemoglobin and fasting insulin data were available. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P < 5 x 10(-8)), 80% of which had no significant evidence of between-ancestry heterogeneity. Analyses restricted to individuals of European ancestry with equivalent sample size would have led to 24 fewer new loci. Compared with single-ancestry analyses, equivalent-sized trans-ancestry fine-mapping reduced the number of estimated variants in 99% credible sets by a median of 37.5%. Genomic-feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways. Our results increase our understanding of diabetes pathophysiology by using trans-ancestry studies for improved power and resolution. A trans-ancestry meta-analysis of GWAS of glycemic traits in up to 281,416 individuals identifies 99 novel loci, of which one quarter was found due to the multi-ancestry approach, which also improves fine-mapping of credible variant sets.Peer reviewe