META-GSA: Combining Findings from Gene-Set Analyses across Several Genome-Wide Association Studies

Introduction

Gene-set analysis (GSA) methods are used as complementary approaches to genome-wide association studies (GWASs). The single marker association estimates of a predefined set of genes are either contrasted with those of all remaining genes or with a null non-associated background. To pool the p-values from several GSAs, it is important to take into account the concordance of the observed patterns resulting from single marker association point estimates across any given gene set. Here we propose an enhanced version of Fisher’s inverse χ²-method META-GSA, however weighting each study to account for imperfect correlation between association patterns.

Simulation and Power

We investigated the performance of META-GSA by simulating GWASs with 500 cases and 500 controls at 100 diallelic markers in 20 different scenarios, simulating different relative risks between 1 and 1.5 in gene sets of 10 genes. Wilcoxon’s rank sum test was applied as GSA for each study. We found that META-GSA has greater power to discover truly associated gene sets than simple pooling of the p-values, by e.g. 59% versus 37%, when the true relative risk for 5 of 10 genes was assume to be 1.5. Under the null hypothesis of no difference in the true association pattern between the gene set of interest and the set of remaining genes, the results of both approaches are almost uncorrelated. We recommend not relying on p-values alone when combining the results of independent GSAs.

Application

We applied META-GSA to pool the results of four case-control GWASs of lung cancer risk (Central European Study and Toronto/Lunenfeld-Tanenbaum Research Institute Study; German Lung Cancer Study and MD Anderson Cancer Center Study), which had already been analyzed separately with four different GSA methods (EASE; SLAT, mSUMSTAT and GenGen). This application revealed the pathway GO0015291 “transmembrane transporter activity” as significantly enriched with associated genes (GSA-method: EASE, p = 0.0315 corrected for multiple testing). Similar results were found for GO0015464 “acetylcholine receptor activity” but only when not corrected for multiple testing (all GSA-methods applied; p≈0.02).     

Physiological responses after two different CrossFit workouts

The present study aimed to investigate the physiological response to CrossFit “workouts of the day” (WODs) based on two different structures of training session: 1) the “as many repetitions as possible” (AMRAP) “Cindy” and 2) the “round for time” (RFT) “Open 18.4” session. CrossFit athletes (11 men and 12 women) were divided into two groups: 1) one performing the WOD “Cindy” (GC) and 2) one performing the WOD “Open 18.4” (GO). Before, immediately after and 30 min after WODs, blood lactate (LAC), heart rate (HR) and systolic and diastolic blood pressures (SBP and DBP) were measured. A two-way ANOVA indicated differences in physiological responses between GC and GO. Both WODs increased HR to similar levels. Only GO significantly increased SBP immediately after exercise compared to the rest period (p < 0.01), with no difference to GC. GO presented higher levels of LAC immediately after exercise compared to GC (15.8 ± 4.9 mM [GO] vs 9.3 ± 2.3 mM [GC]; p < 0.01). LAC remained different between the groups 30 min after exercise (7.0 ± 3.9 mM [GO] vs 3.9 ± 0.9 mM [GC]; p < 0.01). The results suggest that the studied WODs do not differ in acute cardiovascular responses, but depend on different metabolic demands, with RFT structure relying more on glycolytic metabolism (indicated by greater LAC levels after exercise in GO). Such results are in agreement independent of gender.     

Chapter 9: Analyses Using Disease Ontologies

Advanced statistical methods used to analyze high-throughput data such as gene-expression assays result in long lists of “significant genes.” One way to gain insight into the significance of altered expression levels is to determine whether Gene Ontology (GO) terms associated with a particular biological process, molecular function, or cellular component are over- or under-represented in the set of genes deemed significant. This process, referred to as enrichment analysis, profiles a gene-set, and is widely used to makes sense of the results of high-throughput experiments. The canonical example of enrichment analysis is when the output dataset is a list of genes differentially expressed in some condition. To determine the biological relevance of a lengthy gene list, the usual solution is to perform enrichment analysis with the GO. We can aggregate the annotating GO concepts for each gene in this list, and arrive at a profile of the biological processes or mechanisms affected by the condition under study. While GO has been the principal target for enrichment analysis, the methods of enrichment analysis are generalizable. We can conduct the same sort of profiling along other ontologies of interest. Just as scientists can ask “Which biological process is over-represented in my set of interesting genes or proteins?” we can also ask “Which disease (or class of diseases) is over-represented in my set of interesting genes or proteins?“. For example, by annotating known protein mutations with disease terms from the ontologies in BioPortal, Mort et al. recently identified a class of diseases—blood coagulation disorders—that were associated with a 14-fold depletion in substitutions at O-linked glycosylation sites. With the availability of tools for automatic annotation of datasets with terms from disease ontologies, there is no reason to restrict enrichment analyses to the GO. In this chapter, we will discuss methods to perform enrichment analysis using any ontology available in the biomedical domain. We will review the general methodology of enrichment analysis, the associated challenges, and discuss the novel translational analyses enabled by the existence of public, national computational infrastructure and by the use of disease ontologies in such analyses.

What to Learn in This Chapter

• Review the commonly used approach of Gene Ontology based enrichment analysis
• Understand the pitfalls associated with current approaches
• Understand the national infrastructure available for using alternative ontologies for enrichment analysis
• Learn about a generalized enrichment analysis workflow and its application using disease ontologies

This article is part of the “Translational Bioinformatics” collection for PLOS Computational Biology.

     

Classification of Epidermal Growth Factor Receptor Gene Mutation Status Using Serum Proteomic Profiling Predicts Tumor Response in Patients with Stage IIIB or IV Non-Small-Cell Lung Cancer

Objectives

Epidermal growth factor receptor (EGFR) gene mutations in tumors predict tumor response to EGFR tyrosine kinase inhibitors (EGFR-TKIs) in non-small-cell lung cancer (NSCLC). However, obtaining tumor tissue for mutation analysis is challenging. Here, we aimed to detect serum peptides/proteins associated with EGFR gene mutation status, and test whether a classification algorithm based on serum proteomic profiling could be developed to analyze EGFR gene mutation status to aid therapeutic decision-making.

Patients and Methods

Serum collected from 223 stage IIIB or IV NSCLC patients with known EGFR gene mutation status in their tumors prior to therapy was analyzed by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) and ClinProTools software. Differences in serum peptides/proteins between patients with EGFR gene TKI-sensitive mutations and wild-type EGFR genes were detected in a training group of 100 patients; based on this analysis, a serum proteomic classification algorithm was developed to classify EGFR gene mutation status and tested in an independent validation group of 123 patients. The correlation between EGFR gene mutation status, as identified with the serum proteomic classifier and response to EGFR-TKIs was analyzed.

Results

Nine peptide/protein peaks were significantly different between NSCLC patients with EGFR gene TKI-sensitive mutations and wild-type EGFR genes in the training group. A genetic algorithm model consisting of five peptides/proteins (m/z 4092.4, 4585.05, 1365.1, 4643.49 and 4438.43) was developed from the training group to separate patients with EGFR gene TKI-sensitive mutations and wild-type EGFR genes. The classifier exhibited a sensitivity of 84.6% and a specificity of 77.5% in the validation group. In the 81 patients from the validation group treated with EGFR-TKIs, 28 (59.6%) of 47 patients whose matched samples were labeled as “mutant” by the classifier and 3 (8.8%) of 34 patients whose matched samples were labeled as “wild” achieved an objective response (p<0.0001). Patients whose matched samples were labeled as “mutant” by the classifier had a significantly longer progression-free survival (PFS) than patients whose matched samples were labeled as “wild” (p=0.001).

Conclusion

Peptides/proteins related to EGFR gene mutation status were found in the serum. Classification of EGFR gene mutation status using the serum proteomic classifier established in the present study in patients with stage IIIB or IV NSCLC is feasible and may predict tumor response to EGFR-TKIs.     

Characterization of the Denitrification-Associated Phosphorus Uptake Properties of “Candidatus Accumulibacter phosphatis” Clades in Sludge Subjected to Enhanced Biological Phosphorus Removal

To characterize the denitrifying phosphorus (P) uptake properties of “Candidatus Accumulibacter phosphatis,” a sequencing batch reactor (SBR) was operated with acetate. The SBR operation was gradually acclimated from anaerobic-oxic (AO) to anaerobic-anoxic-oxic (A2O) conditions by stepwise increases of nitrate concentration and the anoxic time. The communities of “Ca. Accumulibacter” and associated bacteria at the initial (AO) and final (A2O) stages were compared using 16S rRNA and polyphosphate kinase genes and using fluorescence in situ hybridization (FISH). The acclimation process led to a clear shift in the relative abundances of recognized “Ca. Accumulibacter” subpopulations from clades IIA > IA > IIF to clades IIC > IA > IIF, as well as to increases in the abundance of other associated bacteria (Dechloromonas [from 1.2% to 19.2%] and “Candidatus Competibacter phosphatis” [from 16.4% to 20.0%]), while the overall “Ca. Accumulibacter” abundance decreased (from 55.1% to 29.2%). A series of batch experiments combined with FISH/microautoradiography (MAR) analyses was performed to characterize the denitrifying P uptake properties of the “Ca. Accumulibacter” clades. In FISH/MAR experiments using slightly diluted sludge (∼0.5 g/liter), all “Ca. Accumulibacter” clades successfully took up phosphorus in the presence of nitrate. However, the “Ca. Accumulibacter” clades showed no P uptake in the presence of nitrate when the sludge was highly diluted (∼0.005 g/liter); under these conditions, reduction of nitrate to nitrite did not occur, whereas P uptake by “Ca. Accumulibacter” clades occurred when nitrite was added. These results suggest that the “Ca. Accumulibacter” cells lack nitrate reduction capabilities and that P uptake by “Ca. Accumulibacter” is dependent upon nitrite generated by associated nitrate-reducing bacteria such as Dechloromonas and “Ca. Competibacter.”     

Fossil and Genetic Evidence for the Polyphyletic Nature of the Planktonic Foraminifera "Globigerinoides", and Description of the New Genus Trilobatus

Planktonic foraminifera are one of the most abundant and diverse protists in the oceans. Their utility as paleo proxies requires rigorous taxonomy and comparison with living and genetically related counterparts. We merge genetic and fossil evidence of “Globigerinoides”, characterized by supplementary apertures on spiral side, in a new approach to trace their “total evidence phylogeny” since their first appearance in the latest Paleogene. Combined fossil and molecular genetic data indicate that this genus, as traditionally understood, is polyphyletic. Both datasets indicate the existence of two distinct lineages that evolved independently. One group includes “Globigerinoides” trilobus and its descendants, the extant “Globigerinoides” sacculifer, Orbulina universa and Sphaeroidinella dehiscens. The second group includes the Globigerinoides ruber clade with the extant G. conglobatus and G. elongatus and ancestors. In molecular phylogenies, the trilobus group is not the sister taxon of the ruber group. The ruber group clusters consistently together with the modern Globoturborotalita rubescens as a sister taxon. The re-analysis of the fossil record indicates that the first “Globigerinoides” in the late Oligocene are ancestral to the trilobus group, whereas the ruber group first appeared at the base of the Miocene with representatives distinct from the trilobus group. Therefore, polyphyly of the genus "Globigerinoides" as currently defined can only be avoided either by broadening the genus concept to include G. rubescens and a large number of fossil species without supplementary apertures, or if the trilobus group is assigned to a separate genus. Since the former is not feasible due to the lack of a clear diagnosis for such a broad genus, we erect a new genus Trilobatus for the trilobus group (type species Globigerina triloba Reuss) and amend Globoturborotalita and Globigerinoides to clarify morphology and wall textures of these genera. In the new concept, Trilobatus n. gen. is paraphyletic and gave rise to the Praeorbulina / Orbulina and Sphaeroidinellopsis / Sphaeroidinella lineages.     

Infection Density Dynamics of the Citrus Greening Bacterium “Candidatus Liberibacter asiaticus” in Field Populations of the Psyllid Diaphorina citri and Its Relevance to the Efficiency of Pathogen Transmission to Citrus Plants

Huanglongbing, or citrus greening, is a devastating disease of citrus plants recently spreading worldwide, which is caused by an uncultivable bacterial pathogen, “Candidatus Liberibacter asiaticus,” and vectored by a phloem-sucking insect, Diaphorina citri. We investigated the infection density dynamics of “Ca. Liberibacter asiaticus” in field populations of D. citri with experiments using field-collected insects to address how “Ca. Liberibacter asiaticus” infection density in the vector insect is relevant to pathogen transmission to citrus plants. Of 500 insects continuously collected from “Ca. Liberibacter asiaticus”-infected citrus trees with pathological symptoms in the spring and autumn of 2009, 497 (99.4%) were “Ca. Liberibacter asiaticus” positive. The infections were systemic across head-thorax and abdomen, ranging from 10³ to 10⁷ bacteria per insect. In spring, the infection densities were low in March, at ∼10³ bacteria per insect, increasing up to 10⁶ to 10⁷ bacteria per insect in April and May, and decreasing to 10⁵ to 10⁶ bacteria per insect in late May, whereas the infection densities were constantly ∼10⁶ to 10⁷ bacteria per insect in autumn. Statistical analysis suggested that several factors, such as insect sex, host trees, and collection dates, may be correlated with “Ca. Liberibacter asiaticus” infection densities in field D. citri populations. Inoculation experiments with citrus seedlings using field-collected “Ca. Liberibacter asiaticus”-infected insects suggested that (i) “Ca. Liberibacter asiaticus”-transmitting insects tend to exhibit higher infection densities than do nontransmitting insects, (ii) a threshold level (∼10⁶ bacteria per insect) of “Ca. Liberibacter asiaticus” density in D. citri is required for successful transmission to citrus plants, and (iii) D. citri attaining the threshold infection level transmits “Ca. Liberibacter asiaticus” to citrus plants in a stochastic manner. These findings provide valuable insights into understanding, predicting, and controlling this notorious citrus pathogen.     

XoxF-Type Methanol Dehydrogenase from the Anaerobic Methanotroph “Candidatus Methylomirabilis oxyfera”

“Candidatus Methylomirabilis oxyfera” is a newly discovered anaerobic methanotroph that, surprisingly, oxidizes methane through an aerobic methane oxidation pathway. The second step in this aerobic pathway is the oxidation of methanol. In Gram-negative bacteria, the reaction is catalyzed by pyrroloquinoline quinone (PQQ)-dependent methanol dehydrogenase (MDH). The genome of “Ca. Methylomirabilis oxyfera” putatively encodes three different MDHs that are localized in one large gene cluster: one so-called MxaFI-type MDH and two XoxF-type MDHs (XoxF1 and XoxF2). MxaFI MDHs represent the canonical enzymes, which are composed of two PQQ-containing large (α) subunits (MxaF) and two small (β) subunits (MxaI). XoxF MDHs are novel, ecologically widespread, but poorly investigated types of MDHs that can be phylogenetically divided into at least five different clades. The XoxF MDHs described thus far are homodimeric proteins containing a large subunit only. Here, we purified a heterotetrameric MDH from “Ca. Methylomirabilis oxyfera” that consisted of two XoxF and two MxaI subunits. The enzyme was localized in the periplasm of “Ca. Methylomirabilis oxyfera” cells and catalyzed methanol oxidation with appreciable specific activity and affinity (V_max of 10 μmol min⁻¹ mg⁻¹ protein, K_m of 17 μM). PQQ was present as the prosthetic group, which has to be taken up from the environment since the known gene inventory required for the synthesis of this cofactor is lacking. The MDH from “Ca. Methylomirabilis oxyfera” is the first representative of type 1 XoxF proteins to be described.     

Infection Dynamics of the Tick-Borne Pathogen “Candidatus Neoehrlichia mikurensis” and Coinfections with Borrelia afzelii in Bank Voles in Southern Sweden

The tick-borne bacterium “Candidatus Neoehrlichia mikurensis” has recently been recognized as a human pathogen. Together with Borrelia afzelii, it is one of the most common pathogens found in the tick Ixodes ricinus. Here, we compared the epidemiologies of “Ca. Neoehrlichia mikurensis” and B. afzelii by longitudinal sampling from May to September in one of their most abundant vertebrate hosts, the bank vole (Myodes glareolus), using real-time PCR for detection and quantification. The prevalences of “Ca. Neoehrlichia mikurensis” and B. afzelii were determined to be 19% (50/261) and 22% (56/261), respectively. The prevalence of “Ca. Neoehrlichia mikurensis” increased significantly during the sampling season. The clearance rate of “Ca. Neoehrlichia mikurensis” was significantly higher than that of B. afzelii. We found a high frequency of double infections; 46% of all samples infected with “Ca. Neoehrlichia mikurensis” also had a coinfection with B. afzelii. The frequency of coinfections was significantly higher than expected from the prevalence of each pathogen. The high level of coinfections can be caused by interactions between the pathogens or might reflect variation in general susceptibility among voles.     

Diversity of “Candidatus Liberibacter asiaticus,” Based on the omp Gene Sequence

Huanglongbing (yellow dragon disease) is a destructive disease of citrus. The etiological agent is a noncultured, phloem-restricted alpha-proteobacterium, “Candidatus Liberibacter africanus” in Africa and “Candidatus Liberibacter asiaticus” in Asia. In this study, we used an omp-based PCR-restriction fragment length polymorphism (RFLP) approach to analyze the genetic variability of “Ca. Liberibacter asiaticus” isolates. By using five different enzymes, each the 10 isolates tested could be associated with a specific combination of restriction profiles. The results indicate that the species “Ca. Liberibacter asiaticus,” even within a given region, may comprise several different variants. Thus, omp-based PCR-RFLP analysis is a simple method for detecting and differentiating “Ca. Liberibacter asiaticus” isolates.     

IRG and GBP Host Resistance Factors Target Aberrant, “Non-self” Vacuoles Characterized by the Missing of “Self” IRGM Proteins

Interferon-inducible GTPases of the Immunity Related GTPase (IRG) and Guanylate Binding Protein (GBP) families provide resistance to intracellular pathogenic microbes. IRGs and GBPs stably associate with pathogen-containing vacuoles (PVs) and elicit immune pathways directed at the targeted vacuoles. Targeting of Interferon-inducible GTPases to PVs requires the formation of higher-order protein oligomers, a process negatively regulated by a subclass of IRG proteins called IRGMs. We found that the paralogous IRGM proteins Irgm1 and Irgm3 fail to robustly associate with “non-self” PVs containing either the bacterial pathogen Chlamydia trachomatis or the protozoan pathogen Toxoplasma gondii. Instead, Irgm1 and Irgm3 reside on “self” organelles including lipid droplets (LDs). Whereas IRGM-positive LDs are guarded against the stable association with other IRGs and GBPs, we demonstrate that IRGM-stripped LDs become high affinity binding substrates for IRG and GBP proteins. These data reveal that intracellular immune recognition of organelle-like structures by IRG and GBP proteins is partly dictated by the missing of “self” IRGM proteins from these structures.