Validation of discrete time-to-event prediction models in the presence of competing risks

Clinical prediction models play a key role in risk stratification, therapy assignment and many other fields of medical decision making. Before they can enter clinical practice, their usefulness has to be demonstrated using systematic validation. Methods to assess their predictive performance have been proposed for continuous, binary, and time-to-event outcomes, but the literature on validation methods for discrete time-to-event models with competing risks is sparse. The present paper tries to fill this gap and proposes new methodology to quantify discrimination, calibration, and prediction error (PE) for discrete time-to-event outcomes in the presence of competing risks. In our case study, the goal was to predict the risk of ventilator-associated pneumonia (VAP) attributed to Pseudomonas aeruginosa in intensive care units (ICUs). Competing events are extubation, death, and VAP due to other bacteria. The aim of this application is to validate complex prediction models developed in previous work on more recently available validation data.     

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.     

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.     

Anisotropic intersubunit and inter-ring interactions revealed in the native bullet-shaped chaperonin complex from Thermus thermophilus

Background

The recent morphological studies on chaperonins have revealed that nearly equivalent amount of symmetric GroEL–(GroES)₂ (football-shaped) and asymmetric GroEL–GroES (bullet-shaped) complexes coexist during the chaperonin reaction cycle, which prompted us to reexamine the equatorial split observed for chaperonin from Thermus thermophilus by implementing semi-empirical molecular orbital (MO) calculations, since it is now believed that the symmetric formation is a precursor to the equatorial split.

Methods

Semi-empirical MO calculations were employed to investigate the intersubunit interactions within the bullet-shaped T. thermophilus chaperonin capturing the substrate of folding intermediates. Interaction energies between each cis-GroEL subunit and closely related remaining subunits in cis-GroEL ring, or in trans-GroEL ring across the equatorial plane, and further, interaction energies between each GroES subunit and adjacent subunits in the same GroES ring and in cis-GroEL ring were simulated.

Results

Anisotropic intensities and energy distribution of the subunits were revealed by the calculations, which are consistent with two conformations of the subunits forming cis-GroEL ring as revealed by X-ray crystal structure, and with an anisotropic critical binding site on cis-GroEL ring for chaperonin functioning.

Conclusions

This is the first application of semi-empirical MO calculations to the macromolecular complex of the native bullet-shaped chaperonin (GroEL–GroES–ADP homolog) from T. thermophilus.

General significance

The results also appear to support the occurrence of the equatorial split for T. thermophilus chaperonin observed via electron microscopy, but has not yet been fully observed for Escherichia coli GroEL–GroES system.     

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.     

Functional and evolutionary analysis of DXL1, a non-essential gene encoding a 1-deoxy-D-xylulose 5-phosphate synthase like protein in Arabidopsis thaliana

The synthesis of 1-deoxy-D-xylulose 5-phosphate (DXP), catalyzed by the enzyme DXP synthase (DXS), represents a key regulatory step of the 2-C-methyl-D-erythritol 4-phosphate (MEP) pathway for isoprenoid biosynthesis. In plants DXS is encoded by small multigene families that can be classified into, at least, three specialized subfamilies. Arabidopsis thaliana contains three genes encoding proteins with similarity to DXS, including the well-known DXS1/CLA1 gene, which clusters within subfamily I. The remaining proteins, initially named DXS2 and DXS3, have not yet been characterized. Here we report the expression and functional analysis of A. thaliana DXS2. Unexpectedly, the expression of DXS2 failed to rescue Escherichia coli and A. thaliana mutants defective in DXS activity. Coherently, we found that DXS activity was negligible in vitro, being renamed as DXL1 following recent nomenclature recommendation. DXL1 is targeted to plastids as DXS1, but shows a distinct expression pattern. The phenotypic analysis of a DXL1 defective mutant revealed that the function of the encoded protein is not essential for growth and development. Evolutionary analyses indicated that DXL1 emerged from DXS1 through a recent duplication apparently specific of the Brassicaceae lineage. Divergent selective constraints would have affected a significant fraction of sites after diversification of the paralogues. Furthermore, amino acids subjected to divergent selection and likely critical for functional divergence through the acquisition of a novel, although not yet known, biochemical function, were identified. Our results provide with the first evidences of functional specialization at both the regulatory and biochemical level within the plant DXS family.     

A Japanese child with geleophysic dysplasia caused by a novel mutation of FBN1

Geleophysic dysplasia (GD) is a rare disorder characterized by severe short stature, short hands and feet, limited joint mobility, skin thickening, characteristic facial features (e.g., a “happy” face), and cardiac valvular disorders that often result in an early death. The genes ADAMTSL2 (a disintegrin-like and metalloprotease with thrombospondin type 1 motif-like 2) and FBN1 (fibrillin 1) were recently identified as causative genes for GD. Here, we describe a 10-year-old Japanese female with GD who was born to non-consanguineous parents. At the age of 11 months, she was referred to our hospital because of very short stature for her age (− 4.4 standard deviations of the age-matched value) and a “happy” face with full cheeks, a shortened nose, hypertelorism, and a long and flat philtrum, characteristic of GD. Her hands and feet were small, her skin was thickened, and her joint mobility was generally limited. She had cardiac valvular disorders and history of recurrent respiratory failure. Mutation analysis revealed no abnormalities in ADAMTSL2. However, analysis of FBN1 revealed a novel heterozygous mutation (c.5161T > T/G) in exon 41, which encodes transforming growth factor-β-binding protein-like domain 5 (TB5). GD is an extremely rare disorder and, to our knowledge, only one case of GD with an FBN1 mutation has been reported in Japan. Similar to the previously reported cases of GD, the mutation in the current patient was located in the TB5 domain, which suggests that abnormalities in this domain of FBN1 are responsible for GD.     

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.