Infection-Induced Peroxisome Biogenesis Is a Metabolic Strategy for Herpesvirus Replication.

Viral proteins have evolved to target cellular organelles and usurp their functions for virus replication. Despite the knowledge of these critical functions for several organelles, little is known about peroxisomes during infection. Peroxisomes are primarily metabolic organelles with important functions in lipid metabolism. Here, we discovered that the enveloped viruses human cytomegalovirus (HCMV) and herpes simplex virus type 1 (HSV-1) induce the biogenesis of and unique morphological changes to peroxisomes to support their replication. Targeted proteomic quantification revealed a global virus-induced upregulation of peroxisomal proteins. Mathematical modeling and microscopy structural analysis show that infection triggers peroxisome growth and fission, leading to increased peroxisome numbers and irregular disc-like structures. HCMV-induced peroxisome biogenesis increased the phospholipid plasmalogen, thereby enhancing virus production. Peroxisome regulation and dependence were not observed for the non-enveloped adenovirus. Our findings uncover a role of peroxisomes in viral pathogenesis, with likely implications for multiple enveloped viruses.

Principles of Systems Biology, No. 31.

This month: selected work from the 2018 RECOMB meeting, organized by Ecole Polytechnique and held last April in Paris.

Nonlinear Regression Improves Accuracy of Characterization of Multiplexed Mass Spectrometric Assays.

The need for assay characterization is ubiquitous in quantitative mass spectrometry-based proteomics. Among many assay characteristics, the limit of blank (LOB) and limit of detection (LOD) are two particularly useful figures of merit. LOB and LOD are determined by repeatedly quantifying the observed intensities of peptides in samples with known peptide concentrations and deriving an intensity versus concentration response curve. Most commonly, a weighted linear or logistic curve is fit to the intensity-concentration response, and LOB and LOD are estimated from the fit. Here we argue that these methods inaccurately characterize assays where observed intensities level off at low concentrations, which is a common situation in multiplexed systems. This manuscript illustrates the deficiencies of these methods, and proposes an alternative approach based on nonlinear regression that overcomes these inaccuracies. We evaluated the performance of the proposed method using computer simulations and using eleven experimental data sets acquired in Data-Independent Acquisition (DIA), Parallel Reaction Monitoring (PRM), and Selected Reaction Monitoring (SRM) mode. When the intensity levels off at low concentrations, the nonlinear model changes the estimates of LOB/LOD upwards, in some data sets by 20-40%. In absence of a low concentration intensity leveling off, the estimates of LOB/LOD obtained with nonlinear statistical modeling were identical to those of weighted linear regression. We implemented the nonlinear regression approach in the open-source R-based software MSstats, and advocate its general use for characterization of mass spectrometry-based assays.