Lipid metabolism has been good to me

My career in research has flourished through hard work, supportive mentors, and outstanding mentees and collaborators. The Carman laboratory has contributed to the understanding of lipid metabolism through the isolation and characterization of key lipid biosynthetic enzymes as well as through the identification of the enzyme-encoding genes. Our findings from yeast have proven to be invaluable to understand regulatory mechanisms of human lipid metabolism. Several rewarding aspects of my career have been my service to the Journal of Biological Chemistry as an editorial board member and Associate Editor, the National Institutes of Health as a member of study sections, and national and international scientific meetings as an organizer. I advise early career scientists to not assume anything, acknowledge others’ accomplishments, and pay it forward.     

The ins and outs of phospholipid asymmetry in the plasma membrane: roles in health and disease

A common feature of all eukaryotic membranes is the non-random distribution of different lipid species in the lipid bilayer (lipid asymmetry). Lipid asymmetry provides the two sides of the plasma membrane with different biophysical properties and influences numerous cellular functions. Alteration of lipid asymmetry plays a prominent role during cell fusion, activation of the coagulation cascade, and recognition and removal of apoptotic cell corpses by macrophages (programmed cell clearance). Here we discuss the origin and maintenance of phospholipid asymmetry, based on recent studies in mammalian systems as well as in Caenhorhabditis elegans and other model organisms, along with emerging evidence for a conserved role of mitochondria in the loss of lipid asymmetry during apoptosis. The functional significance of lipid asymmetry and its disruption during health and disease is also discussed.     

Estimating the Toxicity of Pesticide Mixtures to Aquatic Organisms: A Review

A major difficulty in addressing chemical mixtures through legislation or regulations revolves around our limited understanding of their potential impacts. This review provides an overview of recent research on pesticide mixture toxicity to aquatic biota and the methods employed to predict toxic effects. The most common approaches are to assume concentration-addition or independent action of chemicals in a mixture. There are a number of cases in the literature of interactions between pesticides. However, models accounting for possible interactions between mixture components are used infrequently. Although results are limited, studies investigating the effects of pesticide mixtures have not demonstrated significant synergism at environmentally relevant concentrations. Based on the results of our review, we conclude that the concentration-addition model is a generally conservative and practical first-tier model for the ecological assessment of pesticide mixtures in aquatic systems.     

PLRP2 selectively localizes synaptic membrane proteins via acyl-chain remodeling of phospholipids

The plasma membrane of neurons consists of distinct domains, each of which carries specialized functions and a characteristic set of membrane proteins. While this compartmentalized membrane organization is essential for neuronal functions, it remains controversial how neurons establish these domains on the laterally fluid membrane. Here, using immunostaining, lipid-MS analysis and gene ablation with the CRISPR/Cas9 system, we report that the pancreatic lipase-related protein 2 (PLRP2), a phospholipase A1 (PLA1), is a key organizer of membrane protein localization at the neurite tips of PC12 cells. PLRP2 produced local distribution of 1-oleoyl-2-palmitoyl-PC at these sites through acyl-chain remodeling of membrane phospholipids. The resulting lipid domain assembled the syntaxin 4 (Stx4) protein within itself by selectively interacting with the transmembrane domain of Stx4. The localized Stx4, in turn, facilitated the fusion of transport vesicles that contained the dopamine transporter with the domain of the plasma membrane, which led to the localized distribution of the transporter to that domain. These results revealed the pivotal roles of PLA1, specifically PLRP2, in the formation of functional domains in the plasma membrane of neurons. In addition, our results suggest a mode of membrane organization in which the local acyl-chain remodeling of membrane phospholipids controls the selective localization of membrane proteins by regulating both lipid-protein interactions and the fusion of transport vesicles to the lipid domain.     

Integrated evaluation of targeted and non-targeted therapies in a network meta-analysis

Individualized therapies for patients with biomarkers are moving more and more into the focus of research interest when developing new treatments. Hereby, the term individualized (or targeted) therapy denotes a treatment specifically developed for biomarker-positive patients. A network meta-analysis model for a binary endpoint combining the evidence for a targeted therapy from individual patient data with the evidence for a non-targeted therapy from aggregate data is presented and investigated. The biomarker status of the patients is either available at patient-level in individual patient data or at study-level in aggregate data. Both types of biomarker information have to be included. The evidence synthesis model follows a Bayesian approach and applies a meta-regression to the studies with aggregate data. In a simulation study, we address three treatment arms, one of them investigating a targeted therapy. The bias and the root-mean-square error of the treatment effect estimate for the subgroup of biomarker-positive patients based on studies with aggregate data are investigated. Thereby, the meta-regression approach is compared to approaches applying alternative solutions. The regression approach has a surprisingly small bias even in the presence of few studies. By contrast, the root-mean-square error is relatively greater. An illustrative example is provided demonstrating implementation of the presented network meta-analysis model in a clinical setting.     

Flexible Mixture Model Approaches That Accommodate Footprint Size Variability for Robust Detection of Balancing Selection

Long-term balancing selection typically leaves narrow footprints of increased genetic diversity, and therefore most detection approaches only achieve optimal performances when sufficiently small genomic regions (i.e., windows) are examined. Such methods are sensitive to window sizes and suffer substantial losses in power when windows are large. Here, we employ mixture models to construct a set of five composite likelihood ratio test statistics, which we collectively term B statistics. These statistics are agnostic to window sizes and can operate on diverse forms of input data. Through simulations, we show that they exhibit comparable power to the best-performing current methods, and retain substantially high power regardless of window sizes. They also display considerable robustness to high mutation rates and uneven recombination landscapes, as well as an array of other common confounding scenarios. Moreover, we applied a specific version of the B statistics, termed B₂, to a human population-genomic data set and recovered many top candidates from prior studies, including the then-uncharacterized STPG2 and CCDC169–SOHLH2, both of which are related to gamete functions. We further applied B₂ on a bonobo population-genomic data set. In addition to the MHC-DQ genes, we uncovered several novel candidate genes, such as KLRD1, involved in viral defense, and SCN9A, associated with pain perception. Finally, we show that our methods can be extended to account for multiallelic balancing selection and integrated the set of statistics into open-source software named BalLeRMix for future applications by the scientific community.     

Large scale maximum average power multiple inference on time-course count data with application to RNA-seq analysis

Experiments that longitudinally collect RNA sequencing (RNA-seq) data can provide transformative insights in biology research by revealing the dynamic patterns of genes. Such experiments create a great demand for new analytic approaches to identify differentially expressed (DE) genes based on large-scale time-course count data. Existing methods, however, are suboptimal with respect to power and may lack theoretical justification. Furthermore, most existing tests are designed to distinguish among conditions based on overall differential patterns across time, though in practice, a variety of composite hypotheses are of more scientific interest. Finally, some current methods may fail to control the false discovery rate. In this paper, we propose a new model and testing procedure to address the above issues simultaneously. Specifically, conditional on a latent Gaussian mixture with evolving means, we model the data by negative binomial distributions. Motivated by Storey (2007) and Hwang and Liu (2010), we introduce a general testing framework based on the proposed model and show that the proposed test enjoys the optimality property of maximum average power. The test allows not only identification of traditional DE genes but also testing of a variety of composite hypotheses of biological interest. We establish the identifiability of the proposed model, implement the proposed method via efficient algorithms, and demonstrate its good performance via simulation studies. The procedure reveals interesting biological insights, when applied to data from an experiment that examines the effect of varying light environments on the fundamental physiology of the marine diatom Phaeodactylum tricornutum.     

A Bayesian nonparametric testing procedure for paired samples

We propose a Bayesian hypothesis testing procedure for comparing the distributions of paired samples. The procedure is based on a flexible model for the joint distribution of both samples. The flexibility is given by a mixture of Dirichlet processes. Our proposal uses a spike-slab prior specification for the base measure of the Dirichlet process and a particular parametrization for the kernel of the mixture in order to facilitate comparisons and posterior inference. The joint model allows us to derive the marginal distributions and test whether they differ or not. The procedure exploits the correlation between samples, relaxes the parametric assumptions, and detects possible differences throughout the entire distributions. A Monte Carlo simulation study comparing the performance of this strategy to other traditional alternatives is provided. Finally, we apply the proposed approach to spirometry data collected in the United States to investigate changes in pulmonary function in children and adolescents in response to air polluting factors.     

Genetic diseases of the Kennedy pathways for membrane synthesis

The two branches of the Kennedy pathways (CDP-choline and CDP-ethanolamine) are the predominant pathways responsible for the synthesis of the most abundant phospholipids, phosphatidylcholine and phosphatidylethanolamine, respectively, in mammalian membranes. Recently, hereditary diseases associated with single gene mutations in the Kennedy pathways have been identified. Interestingly, genetic diseases within the same pathway vary greatly, ranging from muscular dystrophy to spastic paraplegia to a childhood blinding disorder to bone deformations. Indeed, different point mutations in the same gene (PCYT1; CCTα) result in at least three distinct diseases. In this review, we will summarize and review the genetic diseases associated with mutations in genes of the Kennedy pathway for phospholipid synthesis. These single-gene disorders provide insight, indeed direct genotype-phenotype relationships, into the biological functions of specific enzymes of the Kennedy pathway. We discuss potential mechanisms of how mutations within the same pathway can cause disparate disease.     

Biological control of onion leaf blight disease by bulb and foliar application of powder formulation of antagonist mixture

Abstract

Three antagonists: Pseudomonas fluorescens (Pf1), Bacillus subtilis and Trichoderma viride, were tested alone and in combination for suppression of onion leaf blight (Alternaria palandui) disease under glasshouse and field conditions. The average mean of disease reduction was 24.81% for single strains and 42.44% for mixtures. In addition to disease suppression, treatment with a mixture of antagonists promoted plant growth in terms of increased plant height and ultimately bulb yield. Though seed treatment of either single strain or strain mixtures alone could reduce the disease, subsequent application to root, leaves or soil further reduced the disease and enhanced the plant growth. The mixture consisting of Pseudomonas fluorescens Pf1 plus Bacillus subtilis plus Trichoderma viride was the most effective in reducing the disease and in promoting plant growth and bulb yield in greenhouse and field tests.