Deciphering ApoE Genotype-Driven Proteomic and Lipidomic Alterations in Alzheimer's Disease Across Distinct Brain Regions.

Alzheimer's disease (AD) is a neurodegenerative disease with a complex etiology influenced by confounding factors such as genetic polymorphisms, age, sex, and race. Traditionally, AD research has not prioritized these influences, resulting in dramatically skewed cohorts such as three times the number of Apolipoprotein E (APOE) ε4-allele carriers in AD relative to healthy cohorts. Thus, the resulting molecular changes in AD have previously been complicated by the influence of apolipoprotein E disparities. To explore how apolipoprotein E polymorphism influences AD progression, 62 post-mortem patients consisting of 33 AD and 29 controls (Ctrl) were studied to balance the number of ε4-allele carriers and facilitate a molecular comparison of the apolipoprotein E genotype. Lipid and protein perturbations were assessed across AD diagnosed brains compared to Ctrl brains, ε4 allele carriers (APOE4+ for those carrying 1 or 2 ε4s and APOE4- for non-ε4 carriers), and differences in ε3ε3 and ε3ε4 Ctrl brains across two brain regions (frontal cortex (FCX) and cerebellum (CBM)). The region-specific influences of apolipoprotein E on AD mechanisms showcased mitochondrial dysfunction and cell proteostasis at the core of AD pathophysiology in the post-mortem brains, indicating these two processes may be influenced by genotypic differences and brain morphology.

pmartR 2.0: A Quality Control, Visualization, and Statistics Pipeline for Multiple Omics Datatypes.

The pmartR (https://github.com/pmartR/pmartR) package was designed for the quality control (QC) and analysis of mass spectrometry data, tailored to specific characteristics of proteomic (isobaric or labeled), metabolomic, and lipidomic data sets. Since its initial release, the tool has been expanded to address the needs of its growing userbase and now includes QC and statistics for nuclear magnetic resonance metabolomic data, and leverages the DESeq2, edgeR, and limma-voom R packages for transcriptomic data analyses. These improvements have made progress toward a unified omics processing pipeline for ease of reporting and streamlined statistical purposes. The package's statistics and visualization capabilities have also been expanded by adding support for paired data and by integrating pmartR with the trelliscopejs R package for the quick creation of trellis displays (https://github.com/hafen/trelliscopejs). Here, we present relevant examples of each of these enhancements to pmartR and highlight how each new feature benefits the omics community.

PMart Web Application: Marketplace for Interactive Analysis of Panomics Data.

PMart is a web-based tool for reproducible quality control, exploratory data analysis, statistical analysis, and interactive visualization of 'omics data, based on the functionality of the pmartR R package. The newly improved user interface supports more 'omics data types, additional statistical capabilities, and enhanced options for creating downloadable graphics. PMart supports the analysis of label-free and isobaric-labeled (e.g., TMT, iTRAQ) proteomics, nuclear magnetic resonance (NMR) and mass-spectrometry (MS)-based metabolomics, MS-based lipidomics, and ribonucleic acid sequencing (RNA-seq) transcriptomics data. At the end of a PMart session, a report is available that summarizes the processing steps performed and includes the pmartR R package functions used to execute the data processing. In addition, built-in safeguards in the backend code prevent users from utilizing methods that are inappropriate based on omics data type. PMart is a user-friendly interface for conducting exploratory data analysis and statistical comparisons of omics data without programming.

malbacR: A Package for Standardized Implementation of Batch Correction Methods for Omics Data.

Mass spectrometry is a powerful tool for identifying and analyzing biomolecules such as metabolites and lipids in complex biological samples. Liquid chromatography and gas chromatography mass spectrometry studies quite commonly involve large numbers of samples, which can require significant time for sample preparation and analyses. To accommodate such studies, the samples are commonly split into batches. Inevitably, variations in sample handling, temperature fluctuation, imprecise timing, column degradation, and other factors result in systematic errors or biases of the measured abundances between the batches. Numerous methods are available via R packages to assist with batch correction for omics data; however, since these methods were developed by different research teams, the algorithms are available in separate R packages, each with different data input and output formats. We introduce the malbacR package, which consolidates 11 common batch effect correction methods for omics data into one place so users can easily implement and compare the following: pareto scaling, power scaling, range scaling, ComBat, EigenMS, NOMIS, RUV-random, QC-RLSC, WaveICA2.0, TIGER, and SERRF. The malbacR package standardizes data input and output formats across these batch correction methods. The package works in conjunction with the pmartR package, allowing users to seamlessly include the batch effect correction in a pmartR workflow without needing any additional data manipulation.

Correction: yippee like 3 (ypel3) is a novel gene required for myelinating and perineurial glia development.

[This corrects the article DOI: 10.1371/journal.pgen.1008841.].

yippee like 3 (ypel3) is a novel gene required for myelinating and perineurial glia development.

Hypomyelination, a neurological condition characterized by decreased production of myelin sheets by glial cells, often has no known etiology. Elucidating the genetic causes of hypomyelination provides a better understanding of myelination, as well as means to diagnose, council, and treat patients. Here, we present evidence that YIPPEE LIKE 3 (YPEL3), a gene whose developmental role was previously unknown, is required for central and peripheral glial cell development. We identified a child with a constellation of clinical features including cerebral hypomyelination, abnormal peripheral nerve conduction, hypotonia, areflexia, and hypertrophic peripheral nerves. Exome and genome sequencing revealed a de novo mutation that creates a frameshift in the open reading frame of YPEL3, leading to an early stop codon. We used zebrafish as a model system to validate that YPEL3 mutations are causative of neuropathy. We found that ypel3 is expressed in the zebrafish central and peripheral nervous system. Using CRISPR/Cas9 technology, we created zebrafish mutants carrying a genomic lesion similar to that of the patient. Our analysis revealed that Ypel3 is required for development of oligodendrocyte precursor cells, timely exit of the perineurial glial precursors from the central nervous system (CNS), formation of the perineurium, and Schwann cell maturation. Consistent with these observations, zebrafish ypel3 mutants have metabolomic signatures characteristic of oligodendrocyte and Schwann cell differentiation defects, show decreased levels of Myelin basic protein in the central and peripheral nervous system, and develop defasciculated peripheral nerves. Locomotion defects were observed in adult zebrafish ypel3 mutants. These studies demonstrate that Ypel3 is a novel gene required for perineurial cell development and glial myelination.

Hypergraph models of biological networks to identify genes critical to pathogenic viral response.

BACKGROUND: Representing biological networks as graphs is a powerful approach to reveal underlying patterns, signatures, and critical components from high-throughput biomolecular data. However, graphs do not natively capture the multi-way relationships present among genes and proteins in biological systems. Hypergraphs are generalizations of graphs that naturally model multi-way relationships and have shown promise in modeling systems such as protein complexes and metabolic reactions. In this paper we seek to understand how hypergraphs can more faithfully identify, and potentially predict, important genes based on complex relationships inferred from genomic expression data sets. RESULTS: We compiled a novel data set of transcriptional host response to pathogenic viral infections and formulated relationships between genes as a hypergraph where hyperedges represent significantly perturbed genes, and vertices represent individual biological samples with specific experimental conditions. We find that hypergraph betweenness centrality is a superior method for identification of genes important to viral response when compared with graph centrality. CONCLUSIONS: Our results demonstrate the utility of using hypergraphs to represent complex biological systems and highlight central important responses in common to a variety of highly pathogenic viruses.

Biallelic Mutations in ATP5F1D, which Encodes a Subunit of ATP Synthase, Cause a Metabolic Disorder.

ATP synthase, H+ transporting, mitochondrial F1 complex, δ subunit (ATP5F1D; formerly ATP5D) is a subunit of mitochondrial ATP synthase and plays an important role in coupling proton translocation and ATP production. Here, we describe two individuals, each with homozygous missense variants in ATP5F1D, who presented with episodic lethargy, metabolic acidosis, 3-methylglutaconic aciduria, and hyperammonemia. Subject 1, homozygous for c.245C>T (p.Pro82Leu), presented with recurrent metabolic decompensation starting in the neonatal period, and subject 2, homozygous for c.317T>G (p.Val106Gly), presented with acute encephalopathy in childhood. Cultured skin fibroblasts from these individuals exhibited impaired assembly of F1FO ATP synthase and subsequent reduced complex V activity. Cells from subject 1 also exhibited a significant decrease in mitochondrial cristae. Knockdown of Drosophila ATPsynδ, the ATP5F1D homolog, in developing eyes and brains caused a near complete loss of the fly head, a phenotype that was fully rescued by wild-type human ATP5F1D. In contrast, expression of the ATP5F1D c.245C>T and c.317T>G variants rescued the head-size phenotype but recapitulated the eye and antennae defects seen in other genetic models of mitochondrial oxidative phosphorylation deficiency. Our data establish c.245C>T (p.Pro82Leu) and c.317T>G (p.Val106Gly) in ATP5F1D as pathogenic variants leading to a Mendelian mitochondrial disease featuring episodic metabolic decompensation.

ftmsRanalysis: An R package for exploratory data analysis and interactive visualization of FT-MS data.

The high-resolution and mass accuracy of Fourier transform mass spectrometry (FT-MS) has made it an increasingly popular technique for discerning the composition of soil, plant and aquatic samples containing complex mixtures of proteins, carbohydrates, lipids, lignins, hydrocarbons, phytochemicals and other compounds. Thus, there is a growing demand for informatics tools to analyze FT-MS data that will aid investigators seeking to understand the availability of carbon compounds to biotic and abiotic oxidation and to compare fundamental chemical properties of complex samples across groups. We present ftmsRanalysis, an R package which provides an extensive collection of data formatting and processing, filtering, visualization, and sample and group comparison functionalities. The package provides a suite of plotting methods and enables expedient, flexible and interactive visualization of complex datasets through functions which link to a powerful and interactive visualization user interface, Trelliscope. Example analysis using FT-MS data from a soil microbiology study demonstrates the core functionality of the package and highlights the capabilities for producing interactive visualizations.

Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) peak intensity normalization for complex mixture analyses.

RATIONALE: Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) is a preferred technique for analyzing complex organic mixtures. Currently, there is no consensus normalization approach, nor an objective method for selecting one, for quantitative analyses of FT-ICR-MS data. We investigate a method to evaluate and score the amount of bias various normalization approaches introduce into the data. METHODS: We evaluate the ability of the Statistical Procedure for the Analysis of Normalization Strategies (SPANS) to guide the selection of appropriate normalization approaches for two different FT-ICR-MS data sets. Furthermore, we test the robustness of SPANS results to changes in SPANS parameter values and assess the impact of using various normalization approaches on downstream statistical analyses. RESULTS: The normalization approach identified by SPANS differed for the two data sets. Normalization methods impacted the statistical significance of peaks differently, underscoring the importance of carefully evaluating potential methods. More consistent SPANS scores resulted when at least 120 significant peaks are used, where larger sets of peaks were obtained by increasing the p-value threshold. Interestingly, we show that total sum scaling and highest peak normalization, used in previous studies, underperformed relative to SPANS-recommended normalization approaches. CONCLUSIONS: Although there is no single, best normalization method for all data sets, SPANS provides a mechanism to identify an appropriate normalization method for analyzing FT-ICR-MS data quantitatively. The number of peaks used in the background distributions of SPANS contributes more significantly to the reproducibility of results than the p-value thresholds used to obtain those peaks.

MERS-CoV and H5N1 influenza virus antagonize antigen presentation by altering the epigenetic landscape.

Convergent evolution dictates that diverse groups of viruses will target both similar and distinct host pathways to manipulate the immune response and improve infection. In this study, we sought to leverage this uneven viral antagonism to identify critical host factors that govern disease outcome. Utilizing a systems-based approach, we examined differential regulation of IFN-γ-dependent genes following infection with robust respiratory viruses including influenza viruses [A/influenza/Vietnam/1203/2004 (H5N1-VN1203) and A/influenza/California/04/2009 (H1N1-CA04)] and coronaviruses [severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome CoV (MERS-CoV)]. Categorizing by function, we observed down-regulation of gene expression associated with antigen presentation following both H5N1-VN1203 and MERS-CoV infection. Further examination revealed global down-regulation of antigen-presentation gene expression, which was confirmed by proteomics for both H5N1-VN1203 and MERS-CoV infection. Importantly, epigenetic analysis suggested that DNA methylation, rather than histone modification, plays a crucial role in MERS-CoV-mediated antagonism of antigen-presentation gene expression; in contrast, H5N1-VN1203 likely utilizes a combination of epigenetic mechanisms to target antigen presentation. Together, the results indicate a common mechanism utilized by H5N1-VN1203 and MERS-CoV to modulate antigen presentation and the host adaptive immune response.

Statistically Driven Metabolite and Lipid Profiling of Patients from the Undiagnosed Diseases Network.

Advancements in molecular separations coupled with mass spectrometry have enabled metabolome analyses for clinical cohorts. A population of interest for metabolome profiling is patients with rare disease for which abnormal metabolic signatures may yield clues into the genetic basis, as well as mechanistic drivers of the disease and possible treatment options. We undertook the metabolome profiling of a large cohort of patients with mysterious conditions characterized through the Undiagnosed Diseases Network (UDN). Due to the size and enrollment procedures, collection of the metabolomes for UDN patients took place over 2 years. We describe the study designed to adjust for measurements collected over a long time scale and how this enabled statistical analyses to summarize the metabolome of individual patients. We demonstrate the removal of time-based batch effects, overall statistical characteristics of the UDN population, and two case studies of interest that demonstrate the utility of metabolome profiling for rare diseases.

Lipid Mini-On: mining and ontology tool for enrichment analysis of lipidomic data.

SUMMARY: Here we introduce Lipid Mini-On, an open-source tool that performs lipid enrichment analyses and visualizations of lipidomics data. Lipid Mini-On uses a text-mining process to bin individual lipid names into multiple lipid ontology groups based on the classification (e.g. LipidMaps) and other characteristics, such as chain length. Lipid Mini-On provides users with the capability to conduct enrichment analysis of the lipid ontology terms using a Shiny app with options of five statistical approaches. Lipid classes can be added to customize the user's database and remain updated as new lipid classes are discovered. Visualization of results is available for all classification options (e.g. lipid subclass and individual fatty acid chains). Results are also visualized through an editable network of relationships between the individual lipids and their associated lipid ontology terms. The utility of the tool is demonstrated using biological (e.g. human lung endothelial cells) and environmental (e.g. peat soil) samples. AVAILABILITY AND IMPLEMENTATION: Rodin (R package: https://github.com/PNNL-Comp-Mass-Spec/Rodin), Lipid Mini-On Shiny app (https://github.com/PNNL-Comp-Mass-Spec/LipidMiniOn) and Lipid Mini-On online tool (https://omicstools.pnnl.gov/shiny/lipid-mini-on/). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

P-Mart: Interactive Analysis of Ion Abundance Global Proteomics Data.

The use of mass-spectrometry-based techniques for global protein profiling of biomedical or environmental experiments has become a major focus in research centered on biomarker discovery; however, one of the most important issues recently highlighted in the new era of omics data generation is the ability to perform analyses in a robust and reproducible manner. This has been hypothesized to be one of the issues hindering the ability of clinical proteomics to successfully identify clinical diagnostic and prognostic biomarkers of disease. P-Mart ( https://pmart.labworks.org ) is a new interactive web-based software environment that enables domain scientists to perform quality-control processing, statistics, and exploration of large-complex proteomics data sets without requiring statistical programming. P-Mart is developed in a manner that allows researchers to perform analyses via a series of modules, explore the results using interactive visualization, and finalize the analyses with a collection of output files documenting all stages of the analysis and a report to allow reproduction of the analysis.

pmartR: Quality Control and Statistics for Mass Spectrometry-Based Biological Data.

Prior to statistical analysis of mass spectrometry (MS) data, quality control (QC) of the identified biomolecule peak intensities is imperative for reducing process-based sources of variation and extreme biological outliers. Without this step, statistical results can be biased. Additionally, liquid chromatography-MS proteomics data present inherent challenges due to large amounts of missing data that require special consideration during statistical analysis. While a number of R packages exist to address these challenges individually, there is no single R package that addresses all of them. We present pmartR, an open-source R package, for QC (filtering and normalization), exploratory data analysis (EDA), visualization, and statistical analysis robust to missing data. Example analysis using proteomics data from a mouse study comparing smoke exposure to control demonstrates the core functionality of the package and highlights the capabilities for handling missing data. In particular, using a combined quantitative and qualitative statistical test, 19 proteins whose statistical significance would have been missed by a quantitative test alone were identified. The pmartR package provides a single software tool for QC, EDA, and statistical comparisons of MS data that is robust to missing data and includes numerous visualization capabilities.

Comparing identified and statistically significant lipids and polar metabolites in 15-year old serum and dried blood spot samples for longitudinal studies.

RATIONALE: The use of dried blood spots (DBS) has many advantages over traditional plasma and serum samples such as the smaller blood volume required, storage at room temperature, and ability to sample in remote locations. However, understanding the robustness of different analytes in DBS samples is essential, especially in older samples collected for longitudinal studies. METHODS: Here we analyzed the stability of polar metabolites and lipids in DBS samples collected in 2000-2001 and stored at room temperature. The identified and statistically significant molecules were then compared to matched serum samples stored at -80°C to determine if the DBS samples could be effectively used in a longitudinal study following metabolic disease. RESULTS: A total of 400 polar metabolites and lipids were identified in the serum and DBS samples using gas chromatograph/mass spectrometry (GC/MS), liquid chromatography (LC)/MS, and LC/ion mobility spectrometry-MS (LC/IMS-MS). The identified polar metabolites overlapped well between the sample types, though only one statistically significant metabolite was conserved in a case-control study of older diabetic males with low amounts of high-density lipoproteins and high body mass indices, triacylglycerides and glucose levels when compared to non-diabetic patients with normal levels, indicating that degradation in the DBS samples affects polar metabolite quantitation. Differences in the lipid identifications indicated that some oxidation occurs in the DBS samples. However, 36 statistically significant lipids correlated in both sample types. CONCLUSIONS: The difference in the number of statistically significant polar metabolites and lipids indicated that the lipids did not degrade to as great of a degree as the polar metabolites in the DBS samples and lipid quantitation was still possible. Copyright © 2016 John Wiley & Sons, Ltd.

MPLEx: a method for simultaneous pathogen inactivation and extraction of samples for multi-omics profiling.

The continued emergence and spread of infectious agents is of great concern, and systems biology approaches to infectious disease research can advance our understanding of host-pathogen relationships and facilitate the development of new therapies and vaccines. Molecular characterization of infectious samples outside of appropriate biosafety containment can take place only subsequent to pathogen inactivation. Herein, we describe a modified Folch extraction using chloroform/methanol that facilitates the molecular characterization of infectious samples by enabling simultaneous pathogen inactivation and extraction of proteins, metabolites, and lipids for subsequent mass spectrometry-based multi-omics measurements. This single-sample metabolite, protein and lipid extraction (MPLEx) method resulted in complete inactivation of clinically important bacterial and viral pathogens with exposed lipid membranes, including Yersinia pestis, Salmonella Typhimurium, and Campylobacter jejuni in pure culture, and Yersinia pestis, Campylobacter jejuni, and West Nile, MERS-CoV, Ebola, and influenza H7N9 viruses in infection studies. In addition, >99% inactivation, which increased with solvent exposure time, was also observed for pathogens without exposed lipid membranes including community-associated methicillin-resistant Staphylococcus aureus, Clostridium difficile spores and vegetative cells, and adenovirus type 5. The overall pipeline of inactivation and subsequent proteomic, metabolomic, and lipidomic analyses was evaluated using a human epithelial lung cell line infected with wild-type and mutant influenza H7N9 viruses, thereby demonstrating that MPLEx yields biomaterial of sufficient quality for subsequent multi-omics analyses. Based on these experimental results, we believe that MPLEx will facilitate systems biology studies of infectious samples by enabling simultaneous pathogen inactivation and multi-omics measurements from a single specimen with high success for pathogens with exposed lipid membranes.

Implications of Bioremediation of Polycyclic Aromatic Hydrocarbon-Contaminated Soils for Human Health and Cancer Risk.

Bioremediation uses soil microorganisms to degrade polycyclic aromatic hydrocarbons (PAHs) into less toxic compounds and can be performed in situ, without the need for expensive infrastructure or amendments. This review provides insights into the cancer risks associated with PAH-contaminated soils and places bioremediation outcomes in a context relevant to human health. We evaluated which bioremediation strategies were most effective for degrading PAHs and estimated the cancer risks associated with PAH-contaminated soils. Cancer risk was statistically reduced in 89% of treated soils following bioremediation, with a mean degradation of 44% across the B2 group PAHs. However, all 180 treated soils had postbioremediation cancer risk values that exceeded the U.S. Environmental Protection Agency (USEPA) health-based acceptable risk level (by at least a factor of 2), with 32% of treated soils exceeding recommended levels by greater than 2 orders of magnitude. Composting treatments were most effective at biodegrading PAHs in soils (70% average reduction compared with 28-53% for the other treatment types), which was likely due to the combined influence of the rich source of nutrients and microflora introduced with organic compost amendments. Ultimately, bioremediation strategies, in the studies reviewed, were unable to successfully remove carcinogenic PAHs from contaminated soils to concentrations below the target cancer risk levels recommended by the USEPA.

Simulation study comparing exposure matching with regression adjustment in an observational safety setting with group sequential monitoring.

Sequential methods are well established for randomized clinical trials (RCTs), and their use in observational settings has increased with the development of national vaccine and drug safety surveillance systems that monitor large healthcare databases. Observational safety monitoring requires that sequential testing methods be better equipped to incorporate confounder adjustment and accommodate rare adverse events. New methods designed specifically for observational surveillance include a group sequential likelihood ratio test that uses exposure matching and generalized estimating equations approach that involves regression adjustment. However, little is known about the statistical performance of these methods or how they compare to RCT methods in both observational and rare outcome settings. We conducted a simulation study to determine the type I error, power and time-to-surveillance-end of group sequential likelihood ratio test, generalized estimating equations and RCT methods that construct group sequential Lan-DeMets boundaries using data from a matched (group sequential Lan-DeMets-matching) or unmatched regression (group sequential Lan-DeMets-regression) setting. We also compared the methods using data from a multisite vaccine safety study. All methods had acceptable type I error, but regression methods were more powerful, faster at detecting true safety signals and less prone to implementation difficulties with rare events than exposure matching methods. Method performance also depended on the distribution of information and extent of confounding by site. Our results suggest that choice of sequential method, especially the confounder control strategy, is critical in rare event observational settings. These findings provide guidance for choosing methods in this context and, in particular, suggest caution when conducting exposure matching.

A compendium of multi-omics data illuminating host responses to lethal human virus infections.

Human infections caused by viral pathogens trigger a complex gamut of host responses that limit disease, resolve infection, generate immunity, and contribute to severe disease or death. Here, we present experimental methods and multi-omics data capture approaches representing the global host response to infection generated from 45 individual experiments involving human viruses from the Orthomyxoviridae, Filoviridae, Flaviviridae, and Coronaviridae families. Analogous experimental designs were implemented across human or mouse host model systems, longitudinal samples were collected over defined time courses, and global multi-omics data (transcriptomics, proteomics, metabolomics, and lipidomics) were acquired by microarray, RNA sequencing, or mass spectrometry analyses. For comparison, we have included transcriptomics datasets from cells treated with type I and type II human interferon. Raw multi-omics data and metadata were deposited in public repositories, and we provide a central location linking the raw data with experimental metadata and ready-to-use, quality-controlled, statistically processed multi-omics datasets not previously available in any public repository. This compendium of infection-induced host response data for reuse will be useful for those endeavouring to understand viral disease pathophysiology and network biology.