Deficiency in PHD2-mediated hydroxylation of HIF2α underlies Pacak-Zhuang syndrome.

Pacak-Zhuang syndrome is caused by mutations in the EPAS1 gene, which encodes for one of the three hypoxia-inducible factor alpha (HIFα) paralogs HIF2α and is associated with defined but varied phenotypic presentations including neuroendocrine tumors and polycythemia. However, the mechanisms underlying the complex genotype-phenotype correlations remain incompletely understood. Here, we devised a quantitative method for determining the dissociation constant (Kd) of the HIF2α peptides containing disease-associated mutations and the catalytic domain of prolyl-hydroxylase (PHD2) using microscale thermophoresis (MST) and showed that neuroendocrine-associated Class 1 HIF2α mutants have distinctly higher Kd than the exclusively polycythemia-associated Class 2 HIF2α mutants. Based on the co-crystal structure of PHD2/HIF2α peptide complex at 1.8 Å resolution, we showed that the Class 1 mutated residues are localized to the critical interface between HIF2α and PHD2, adjacent to the PHD2 active catalytic site, while Class 2 mutated residues are localized to the more flexible region of HIF2α that makes less contact with PHD2. Concordantly, Class 1 mutations were found to significantly increase HIF2α-mediated transcriptional activation in cellulo compared to Class 2 counterparts. These results reveal a structural mechanism in which the strength of the interaction between HIF2α and PHD2 is at the root of the general genotype-phenotype correlations observed in Pacak-Zhuang syndrome.

Editorial, special issue on "Advances in Robust Statistics".

Starting with 2020 volume, the journal Metron has decided to celebrate the centenary since its foundation with three special issues. This volume is dedicated to robust statistics. A striking feature of most applied statistical analyses is the use of methods that are well known to be sensitive to outliers or to other departures from the postulated model. Robust statistical methods provide useful tools for reducing this sensitivity, through the detection of the outliers by first fitting the majority of the data and then by flagging deviant data points. The six papers in this issue cover a wide orientation in all fields of robustness. This editorial first provides some facts about the history and current state of robust statistics and then summarizes the contents of each paper.

Detecting outlying samples in a parallel factor analysis model.

To explore multi-way data, different methods have been proposed. Here, we study the popular PARAFAC (Parallel factor analysis) model, which expresses multi-way data in a more compact way, without ignoring the underlying complex structure. To estimate the score and loading matrices, an alternating least squares procedure is typically used. It is however well known that least squares techniques suffer from outlying observations, making the models useless when outliers are present in the data. In this paper, we present a robust PARAFAC method. Essentially, it searches for an outlier-free subset of the data, on which we can then perform the classical PARAFAC algorithm. An outlier map is constructed to identify outliers. Simulations and examples show the robustness of our approach.

Robust PCA and classification in biosciences.

MOTIVATION: Principal components analysis (PCA) is a very popular dimension reduction technique that is widely used as a first step in the analysis of high-dimensional microarray data. However, the classical approach that is based on the mean and the sample covariance matrix of the data is very sensitive to outliers. Also, classification methods based on this covariance matrix do not give good results in the presence of outlying measurements. RESULTS: First, we propose a robust PCA (ROBPCA) method for high-dimensional data. It combines projection-pursuit ideas with robust estimation of low-dimensional data. We also propose a diagnostic plot to display and classify the outliers. This ROBPCA method is applied to several bio-chemical datasets. In one example, we also apply a robust discriminant method on the scores obtained with ROBPCA. We show that this combination of robust methods leads to better classifications than classical PCA and quadratic discriminant analysis. AVAILABILITY: All the programs are part of the Matlab Toolbox for Robust Calibration, available at http://www.wis.kuleuven.ac.be/stat/robust.html.

Lack of fluorophotometric evidence of aqueous-vitreous barrier disruption after posterior capsulorhexis.

PURPOSE: To evaluate the integrity of the aqueous-vitreous barrier by assessing the flow of fluorescein from the anterior chamber to the anterior vitreous using fluorophotometry in eyes with a posterior continuous curvilinear capsulorhexis (PCCC) and in eyes without a PCCC. SETTING: University Hospital Antwerp, Edegem, Belgium. METHODS: Ten patients had bilateral extracapsular cataract extraction with implantation of an intraocular lens. In 1 eye, a PCCC was performed; the other eye served as a negative control. The eyes of 2 other patients who had complicated cataract surgery with posterior capsule and anterior hyaloid membrane rupture served as positive controls. All patients had fluorophotometry of both eyes 12 to 18 months after surgery to measure the flow of fluorescein from the anterior chamber to the anterior vitreous. RESULTS: There were no statistically significant differences in the distribution pattern of fluorescein between eyes with PCCC and eyes without PCCC. In contrast, enhanced flow was detected in both eyes with rupture of the posterior capsule and the anterior hyaloid. CONCLUSIONS: In this fluorophotometry study, a PCCC did not seem to disrupt the aqueous-vitreous barrier. Results indicate that an intact anterior vitreous membrane is crucial to maintain the barrier function between the anterior and the posterior segments of the eye.