Binding of an antibody mimetic of the human low density lipoprotein receptor to apolipoprotein E is governed through electrostatic forces. Studies using site-directed mutagenesis and molecular modeling.

Monoclonal antibody 2E8 is specific for an epitope that coincides with the binding site of the low density lipoprotein receptor (LDLR) on human apoE. Its reactivity with apoE variants resembles that of the LDLR: it binds well with apoE3 and poorly with apoE2. The heavy chain complementarity-determining region (CDRH) 2 of 2E8 shows homology to the ligand-binding domain of the LDLR. To define better the structural basis of the 2E8/apoE interaction and particularly the role of electrostatic interactions, we generated and characterized a panel of 2E8 variants. Replacement of acidic residues in the 2E8 CDRHs showed that Asp52, Glu53, and Asp56 are essential for high-affinity binding. Although Asp31 (CDRH1), Glu58 (CDRH2), and Asp97 (CDRH3) did not appear to be critical, the Asp97 → Ala variant acquired reactivity with apoE2. A Thr57 → Glu substitution increased affinity for both apoE3 and apoE2. The affinities of wild-type 2E8 and variants for apoE varied inversely with ionic strength, suggesting that electrostatic forces contribute to both antigen binding and isoform specificity. We propose a model of the 2E8·apoE immune complex that is based on the 2E8 and apoE crystal structures and that is consistent with the apoE-binding properties of wild-type 2E8 and its variants. Given the similarity between the LDLR and 2E8 in terms of specificity, the LDLR/ligand interaction may also have an important electrostatic component

Results of the 2016 Indianapolis Biodiversity Survey, Marion County, Indiana

Surprising biodiversity can be found in cities, but urban habitats are understudied. We report on a bioblitz conducted primarily within a 24-hr period on September 16 and 17, 2016 in Indianapolis, Indiana, USA. The event focused on stretches of three waterways and their associated riparian habitat: Fall Creek (20.6 ha; 51 acres), Pleasant Run (23.5 ha; 58 acres), and Pogue’s Run (27.1 ha; 67 acres). Over 75 scientists, naturalists, students, and citizen volunteers comprised 14 different taxonomic teams. Five hundred ninety taxa were documented despite the rainy conditions. A brief summary of the methods and findings are presented here. Detailed maps of survey locations and inventory results are available on the Indiana Academy of Science website (https://www.indianaacademyofscience.org/)

DNA methylation-based biological age, genome-wide average DNA methylation, and conventional breast cancer risk factors.

DNA methylation-based biological age (DNAm age), as well as genome-wide average DNA methylation, have been reported to predict breast cancer risk. We aimed to investigate the associations between these DNA methylation-based risk factors and 18 conventional breast cancer risk factors for disease-free women. A sample of 479 individuals from the Australian Mammographic Density Twins and Sisters was used for discovery, a sample of 3354 individuals from the Melbourne Collaborative Cohort Study was used for replication, and meta-analyses pooling results from the two studies were conducted. DNAm age based on three epigenetic clocks (Hannum, Horvath and Levine) and genome-wide average DNA methylation were calculated using the HumanMethylation 450 K BeadChip assay data. The DNAm age measures were positively associated with body mass index (BMI), smoking, alcohol drinking and age at menarche (all nominal P < 0.05). Genome-wide average DNA methylation was negatively associated with smoking and number of live births, and positively associated with age at first live birth (all nominal P < 0.05). The association of DNAm age with BMI was also evident in within-twin-pair analyses that control for familial factors. This study suggests that some lifestyle and hormonal risk factors are associated with these DNA methylation-based breast cancer risk factors, and the observed associations are unlikely to be due to familial confounding but are likely causal. DNA methylation-based risk factors could interplay with conventional risk factors in modifying breast cancer risk

The National Cancer Institute Cohort Consortium : An International Pooling Collaboration of 58 Cohorts from 20 Countries

Cohort studies have been central to the establishment of the known causes of cancer. To dissect cancer etiology in more detail-for instance, for personalized risk prediction and prevention, assessment of risks of subtypes of cancer, and assessment of small elevations in risk-there is a need for analyses of far larger cohort datasets than available in individual existing studies. To address these challenges, the NCI Cohort Consortium was founded in 2001. It brings together 58 cancer epidemiology cohorts from 20 countries to undertake large-scale pooling research. The cohorts in aggregate include over nine million study participants, with biospecimens available for about two million of these. Research in the Consortium is undertaken by >40 working groups focused on specific cancer sites, exposures, or other research areas. More than 180 publications have resulted from the Consortium, mainly on genetic and other cancer epidemiology, with high citation rates. This article describes the foundation of the Consortium; its structure, governance, and methods of working; the participating cohorts; publications; and opportunities. The Consortium welcomes newmembers with cancer-oriented cohorts of 10,000 or more participants and an interest in collaborative research. (C) 2018 AACR.Peer reviewe

Prospective Evaluation over 15 Years of Six Breast Cancer Risk Models.

Prospective validation of risk models is needed to assess their clinical utility, particularly over the longer term. We evaluated the performance of six commonly used breast cancer risk models (IBIS, BOADICEA, BRCAPRO, BRCAPRO-BCRAT, BCRAT, and iCARE-lit). 15-year risk scores were estimated using lifestyle factors and family history measures from 7608 women in the Melbourne Collaborative Cohort Study who were aged 50-65 years and unaffected at commencement of follow-up two (conducted in 2003-2007), of whom 351 subsequently developed breast cancer. Risk discrimination was assessed using the C-statistic and calibration using the expected/observed number of incident cases across the spectrum of risk by age group (50-54, 55-59, 60-65 years) and family history of breast cancer. C-statistics were higher for BOADICEA (0.59, 95% confidence interval (CI) 0.56-0.62) and IBIS (0.57, 95% CI 0.54-0.61) than the other models (p-difference ≤ 0.04). No model except BOADICEA calibrated well across the spectrum of 15-year risk (p-value < 0.03). The performance of BOADICEA and IBIS was similar across age groups and for women with or without a family history. For middle-aged Australian women, BOADICEA and IBIS had the highest discriminatory accuracy of the six risk models, but apart from BOADICEA, no model was well-calibrated across the risk spectrum.This work was primarily supported by grant 1129136 from the Australian National Health and Medical Research Council (NHMRC) (https://www.nhmrc.gov.au/). MCCS cohort recruitment was funded by Cancer Council Victoria (https://www.cancervic.org.au/) and VicHealth (https://www.vichealth.vic.gov.au/). The MCCS was further supported by Australian NHMRC grants 209057, 396414 and 1074383, and ongoing follow-up and data management has been funded by Cancer Council Victoria since 1995. Cases and their vital status were ascertained through the Victorian Cancer Registry and the Australian Institute of Health and Welfare, including the National Death Index and the Australian Cancer Database.TN-D is a recipient of a Career Development Fellowship from the National Breast Cancer Foundation (Australia). JLH and MCS are Senior Principal and Senior Research Fellows of the National Health and Medical Research Council (Australia), respectively. ACA and AJL are supported by grants from Cancer Research UK (C12292/A20861 and PPRPGM19 Nov20\100002)