Mass spectrometry-based high-throughput proteomics and its role in biomedical studies and systems biology

There are multiple reasons why the next generation of biological and medical studies require increasing numbers of samples. Biological systems are dynamic, and the effect of a perturbation depends on the genetic background and environment. As a consequence, many conditions need to be considered to reach generalizable conclusions. Moreover, human population and clinical studies only reach sufficient statistical power if conducted at scale and with precise measurement methods. Finally, many proteins remain without sufficient functional annotations, because they have not been systematically studied under a broad range of conditions. In this review, we discuss the latest technical developments in mass spectrometry (MS)-based proteomics that facilitate large-scale studies by fast and efficient chromatography, fast scanning mass spectrometers, data-independent acquisition (DIA), and new software. We further highlight recent studies which demonstrate how high-throughput (HT) proteomics can be applied to capture biological diversity, to annotate gene functions or to generate predictive and prognostic models for human diseases.     

Simultaneous modeling of multivariate heterogeneous responses and heteroskedasticity via a two-stage composite likelihood

Multivariate heterogeneous responses and heteroskedasticity have attracted increasing attention in recent years. In genome-wide association studies, effective simultaneous modeling of multiple phenotypes would improve statistical power and interpretability. However, a flexible common modeling system for heterogeneous data types can pose computational difficulties. Here we build upon a previous method for multivariate probit estimation using a two-stage composite likelihood that exhibits favorable computational time while retaining attractive parameter estimation properties. We extend this approach to incorporate multivariate responses of heterogeneous data types (binary and continuous), and possible heteroskedasticity. Although the approach has wide applications, it would be particularly useful for genomics, precision medicine, or individual biomedical prediction. Using a genomics example, we explore statistical power and confirm that the approach performs well for hypothesis testing and coverage percentages under a wide variety of settings. The approach has the potential to better leverage genomics data and provide interpretable inference for pleiotropy, in which a locus is associated with multiple traits.     

A missense variant in the NSDHL gene in a Chihuahua with a congenital cornification disorder resembling inflammatory linear verrucous epidermal nevi

Congenital hemidysplasia with ichthyosiform nevus and limb defects syndrome in humans is a genodermatosis characterized by inflammatory linear verrucous epidermal nevi (ILVEN), often showing a striking lateralization pattern. It is caused by variants in the NSDHL gene encoding a 3β‐hydroxysteroid dehydrogenase involved in the cholesterol biosynthesis pathway. In the present study, we investigated a female Chihuahua, which showed clinical and histological signs of ILVEN. We performed a candidate gene analysis in the affected animal. This analysis revealed a single missense variant in the NSDHL gene in the affected dog (XM_014111859.2:c.700G>A). The variant is predicted to cause a non‐conservative amino acid change from glycine to arginine, XP_013967334.1:p.(Gly234Arg). The mutant allele was absent from WGS data of 594 genetically diverse dogs and eight wolves. Sanger sequencing confirmed that the variant was heterozygous in the affected dog and absent from 22 control Chihuahuas. Based on the knowledge about the functional impact of NSDHL variants in dogs and other species, c.700G>A is probably pathogenic and a convincing candidate causative variant for the observed skin lesions in the affected Chihuahua.     

Statistical and Economic Techniques for Site-specific Nematode Management

Recent advances in precision agriculture technologies and spatial statistics allow realistic, site-specific estimation of nematode damage to field crops and provide a platform for the site-specific delivery of nematicides within individual fields. This paper reviews the spatial statistical techniques that model correlations among neighboring observations and develop a spatial economic analysis to determine the potential of site-specific nematicide application. The spatial econometric methodology applied in the context of site-specific crop yield response contributes to closing the gap between data analysis and realistic site-specific nematicide recommendations and helps to provide a practical method of site-specifically controlling nematodes.     

MED12 and BRD4 cooperate to sustain cancer growth upon loss of mediator kinase

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Edge distributions of Ceutorhynchus assimilis and its parasitoid Trichomalus perfectus in a crop of winter oilseed rape (Brassica napus)

The edge distributions of Ceutorhynchusassimilis and its parasitoid Trichomalusperfectus within a crop of winter oilseed rape were investigated between May and July using yellow water-traps arranged on a grid system. A randomisation test, which compared the observed arrangement of trap counts with permuted arrangements, was used to quantify the observed tendency of C. assimilisto congregate at the edges as it migrated into the crop; this tendency declined during the season. Trichomalus perfectus were distributed at the edge of the crop only during the early phase of its immigration in May, which occurred later than immigration of its host. It is considered that this simple means of quantifying insect spatial distribution could prove a useful tool in precision targeting of insecticides. This revised version was published online in July 2006 with corrections to the Cover Date.