Outlier detection methods are still effective even using virtual species created with the probabilistic approach

Liu et al. (Journal of Biogeography, 2018, 45 :164–176) presented an approach to detect outliers in species distribution data by developing virtual species created using the threshold approach. Meynard et al. (Journal of biogeography, 2019, 46 :2141–2144) raised concerns about this approach stating that ‘using a probabilistic approach … may significantly change results’. Here we provide a new series of simulations using the two approaches and demonstrate that the outlier detection approach based on pseudo species distribution models was still effective when using the probabilistic approach, although the detection rate was lower than when using the threshold approach.     

SIMEX for correction of dietary exposure effects with Box-Cox transformed data

Modelling dietary data, and especially 24-hr dietary recall (24HDR) data, is a challenge. Ignoring the inherent measurement error (ME) leads to biased effect estimates when the association between an exposure and an outcome is investigated. We propose an adapted simulation extrapolation (SIMEX) algorithm for modelling dietary exposures. For this purpose, we exploit the ME model of the NCI method where we assume the assumption of normally distributed errors of the reported intake on the Box-Cox transformed scale and of unbiased recalls on the original scale. According to the SIMEX algorithm, remeasurements of the observed data with additional ME are generated in order to estimate the association between the level of ME and the resulting effect estimate. Subsequently, this association is extrapolated to the case of zero ME to obtain the corrected estimate. We show that the proposed method fulfils the key property of the SIMEX approach, that is, that the MSE of the generated data will converge to zero if the ME variance converges to zero. Furthermore, the method is applied to real 24HDR data of the I.Family study to correct the effects of salt and alcohol intake on blood pressure. In a simulation study, the method is compared with the NCI method resulting in effect estimates with either smaller MSE or smaller bias in certain situations. In addition, we found our method to be more informative and easier to implement. Therefore, we conclude that the proposed method is useful to promote the dissemination of ME correction methods in nutritional epidemiology.     

Bayesian design of biosimilars clinical programs involving multiple therapeutic indications

In this paper, we propose a Bayesian design framework for a biosimilars clinical program that entails conducting concurrent trials in multiple therapeutic indications to establish equivalent efficacy for a proposed biologic compared to a reference biologic in each indication to support approval of the proposed biologic as a biosimilar. Our method facilitates information borrowing across indications through the use of a multivariate normal correlated parameter prior (CPP), which is constructed from easily interpretable hyperparameters that represent direct statements about the equivalence hypotheses to be tested. The CPP accommodates different endpoints and data types across indications (eg, binary and continuous) and can, therefore, be used in a wide context of models without having to modify the data (eg, rescaling) to provide reasonable information-borrowing properties. We illustrate how one can evaluate the design using Bayesian versions of the type I error rate and power with the objective of determining the sample size required for each indication such that the design has high power to demonstrate equivalent efficacy in each indication, reasonably high power to demonstrate equivalent efficacy simultaneously in all indications (ie, globally), and reasonable type I error control from a Bayesian perspective. We illustrate the method with several examples, including designing biosimilars trials for follicular lymphoma and rheumatoid arthritis using binary and continuous endpoints, respectively.     

Approximation of bias and mean-squared error in two-sample Mendelian randomization analyses

Mendelian randomization (MR) is a type of instrumental variable (IV) analysis that uses genetic variants as IVs for a risk factor to study its causal effect on an outcome. Extensive investigations on the performance of IV analysis procedures, such as the one based on the two-stage least squares (2SLS) procedure, have been conducted under the one-sample scenario, where measures on IVs, the risk factor, and the outcome are assumed to be available for each study participant. Recent MR analysis usually is performed with data from two independent or partially overlapping genetic association studies (two-sample setting), with one providing information on the association between the IVs and the outcome, and the other on the association between the IVs and the risk factor. We investigate the performance of 2SLS in the two-sample–based MR when the IVs are weakly associated with the risk factor. We derive closed form formulas for the bias and mean squared error of the 2SLS estimate and verify them with numeric simulations under realistic circumstances. Using these analytic formulas, we can study the pros and cons of conducting MR analysis under one-sample and two-sample settings and assess the impact of having overlapping samples. We also propose and validate a bias-corrected estimator for the causal effect.     

The potentiation of myeloperoxidase activity by the glycosaminoglycan-dependent binding of myeloperoxidase to proteins of the extracellular matrix

Background

Myeloperoxidase (MPO) is an abundant hemoprotein expressed by neutrophil granulocytes that is recognized to play an important role in the development of vascular diseases. Upon degranulation from circulating neutrophil granulocytes, MPO binds to the surface of endothelial cells in an electrostatic-dependent manner and undergoes transcytotic migration to the underlying extracellular matrix (ECM). However, the mechanisms governing the binding of MPO to subendothelial ECM proteins, and whether this binding modulates its enzymatic functions are not well understood.

Methods

We investigated MPO binding to ECM derived from aortic endothelial cells, aortic smooth muscle cells, and fibroblasts, and to purified ECM proteins, and the modulation of these associations by glycosaminoglycans. The oxidizing and chlorinating potential of MPO upon binding to ECM proteins was tested.

Results

MPO binds to the ECM proteins collagen IV and fibronectin, and this association is enhanced by the pre-incubation of these proteins with glycosaminoglycans. Correspondingly, an excess of glycosaminoglycans in solution during incubation inhibits the binding of MPO to collagen IV and fibronectin. These observations were confirmed with cell-derived ECM. The oxidizing and chlorinating potential of MPO was preserved upon binding to collagen IV and fibronectin; even the potentiation of MPO activity in the presence of collagen IV and fibronectin was observed.

Conclusions

Collectively, the data reveal that MPO binds to ECM proteins on the basis of electrostatic interactions, and MPO chlorinating and oxidizing activity is potentiated upon association with these proteins.

General significance

Our findings provide new insights into the molecular mechanisms underlying the interaction of MPO with ECM proteins.     

Honokiol: A non-adipogenic PPARγ agonist from nature

Background

Peroxisome proliferator-activated receptor gamma (PPARγ) agonists are clinically used to counteract hyperglycemia. However, so far experienced unwanted side effects, such as weight gain, promote the search for new PPARγ activators.

Methods

We used a combination of in silico, in vitro, cell-based and in vivo models to identify and validate natural products as promising leads for partial novel PPARγ agonists.

Results

The natural product honokiol from the traditional Chinese herbal drug Magnolia bark was in silico predicted to bind into the PPARγ ligand binding pocket as dimer. Honokiol indeed directly bound to purified PPARγ ligand-binding domain (LBD) and acted as partial agonist in a PPARγ-mediated luciferase reporter assay. Honokiol was then directly compared to the clinically used full agonist pioglitazone with regard to stimulation of glucose uptake in adipocytes as well as adipogenic differentiation in 3T3-L1 pre-adipocytes and mouse embryonic fibroblasts. While honokiol stimulated basal glucose uptake to a similar extent as pioglitazone, it did not induce adipogenesis in contrast to pioglitazone. In diabetic KKAy mice oral application of honokiol prevented hyperglycemia and suppressed weight gain.

Conclusion

We identified honokiol as a partial non-adipogenic PPARγ agonist in vitro which prevented hyperglycemia and weight gain in vivo.

General significance

This observed activity profile suggests honokiol as promising new pharmaceutical lead or dietary supplement to combat metabolic disease, and provides a molecular explanation for the use of Magnolia in traditional medicine.     

Random forests-based differential analysis of gene sets for gene expression data

In DNA microarray studies, gene-set analysis (GSA) has become the focus of gene expression data analysis. GSA utilizes the gene expression profiles of functionally related gene sets in Gene Ontology (GO) categories or priori-defined biological classes to assess the significance of gene sets associated with clinical outcomes or phenotypes. Many statistical approaches have been proposed to determine whether such functionally related gene sets express differentially (enrichment and/or deletion) in variations of phenotypes. However, little attention has been given to the discriminatory power of gene sets and classification of patients.     

Host genetic risk factors for community-acquired pneumonia

This study was conducted to establish the contribution of genetic host factors in the susceptibility to community acquired pneumonia (CAP) in the Russian population. Patients with CAP (n = 334), volunteers without a previous history of CAP, constantly exposed to infectious agents, control A group (n = 141) and a second control group B consisted of healthy persons (n = 314) were included in the study. All subjects were genotyped for 13 polymorphic variants in the genes of xenobiotics detoxification CYP1A1 (rs2606345, rs4646903, and rs1048943), GSTM1 (Ins/del), GSTT1 (Ins/del), ABCB1 rs1045642); immune and inflammation response IL-6 (rs1800795), TNF-a (rs1800629), MBL2 (rs7096206), CCR5 (rs333), NOS3 (rs1799983), angiotensin-converting enzyme ACE (rs4340), and occlusive vascular disease/hyperhomocysteinemia MTHFR (rs1801133). Seven polymorphic variants in genes CYP1A1, GSTM1, ABCB1, NOS3, IL6, CCR5 and ACE were associated with CAP. For two genes CYP1A1 and GSTM1 associations remained significant after correction for multiple comparisons. Multiple analysis by the number of all risk genotypes showed a highly significant association with CAP (P = 2.4 × 10^− 7, OR = 3.03, 95% CI 1.98–4.64) with the threshold for three risk genotypes. Using the ROC-analysis, the AUC value for multi-locus model was estimated as 68.38.