The nasal microbiome in asthma.

BACKGROUND: Nasal microbiota may influence asthma pathobiology. OBJECTIVE: We sought to characterize the nasal microbiome of subjects with exacerbated asthma, nonexacerbated asthma, and healthy controls to identify nasal microbiota associated with asthma activity. METHODS: We performed 16S ribosomal RNA sequencing on nasal swabs obtained from 72 primarily adult subjects with exacerbated asthma (n = 20), nonexacerbated asthma (n = 31), and healthy controls (n = 21). Analyses were performed using Quantitative Insights into Microbial (QIIME); linear discriminant analysis effect size (LEfSe); Phylogenetic Investigation of Communities by Reconstruction of Unobserved States; and Statistical Analysis of Metagenomic Profiles (PICRUSt); and Statistical Analysis of Metagenomic Profiles (STAMP). Species found to be associated with asthma activity were validated using quantitative PCR. Metabolic pathways associated with differentially abundant nasal taxa were inferred through metagenomic functional prediction. RESULTS: Nasal bacterial composition significantly differed among subjects with exacerbated asthma, nonexacerbated asthma, and healthy controls (permutational multivariate ANOVA, P = 2.2 × 10-2). Relative to controls, the nasal microbiota of subjects with asthma were enriched with taxa from Bacteroidetes (Wilcoxon-Mann-Whitney, r = 0.33, P = 5.1 × 10-3) and Proteobacteria (r = 0.29, P = 1.4 × 10-2). Four species were differentially abundant based on asthma status after correction for multiple comparisons: Prevotella buccalis, Padj = 1.0 × 10-2; Dialister invisus, Padj = 9.1 × 10-3; Gardnerella vaginalis, Padj = 2.8 × 10-3; Alkanindiges hongkongensis, Padj = 2.6 × 10-3. These phyla and species were also differentially abundant based on asthma activity (exacerbated asthma vs nonexacerbated asthma vs controls). Quantitative PCR confirmed species overrepresentation in asthma relative to controls for Prevotella buccalis (fold change = 130, P = 2.1 × 10-4) and Gardnerella vaginalis (fold change = 160, P = 6.8 × 10-4). Metagenomic inference revealed differential glycerolipid metabolism (Kruskal-Wallis, P = 1.9 × 10-4) based on asthma activity. CONCLUSIONS: Nasal microbiome composition differs in subjects with exacerbated asthma, nonexacerbated asthma, and healthy controls. The identified nasal taxa could be further investigated for potential mechanistic roles in asthma and as possible biomarkers of asthma activity.

Network-based approaches that exploit inferred transcription factor activity to analyze the impact of genetic variation on gene expression.

Over the past decade, a number of methods have emerged for inferring protein-level transcription factor activities in individual samples based on prior information about the structure of the gene regulatory network. We discuss how this has enabled new methods for dissecting trans-acting mechanisms that underpin genetic variation in gene expression.

Identifying genetic modulators of the connectivity between transcription factors and their transcriptional targets.

Regulation of gene expression by transcription factors (TFs) is highly dependent on genetic background and interactions with cofactors. Identifying specific context factors is a major challenge that requires new approaches. Here we show that exploiting natural variation is a potent strategy for probing functional interactions within gene regulatory networks. We developed an algorithm to identify genetic polymorphisms that modulate the regulatory connectivity between specific transcription factors and their target genes in vivo. As a proof of principle, we mapped connectivity quantitative trait loci (cQTLs) using parallel genotype and gene expression data for segregants from a cross between two strains of the yeast Saccharomyces cerevisiae We identified a nonsynonymous mutation in the DIG2 gene as a cQTL for the transcription factor Ste12p and confirmed this prediction empirically. We also identified three polymorphisms in TAF13 as putative modulators of regulation by Gcn4p. Our method has potential for revealing how genetic differences among individuals influence gene regulatory networks in any organism for which gene expression and genotype data are available along with information on binding preferences for transcription factors.

Harnessing natural sequence variation to dissect posttranscriptional regulatory networks in yeast.

Understanding how genomic variation influences phenotypic variation through the molecular networks of the cell is one of the central challenges of biology. Transcriptional regulation has received much attention, but equally important is the posttranscriptional regulation of mRNA stability. Here we applied a systems genetics approach to dissect posttranscriptional regulatory networks in the budding yeast Saccharomyces cerevisiae. Quantitative sequence-to-affinity models were built from high-throughput in vivo RNA binding protein (RBP) binding data for 15 yeast RBPs. Integration of these models with genome-wide mRNA expression data allowed us to estimate protein-level RBP regulatory activity for individual segregants from a genetic cross between two yeast strains. Treating these activities as a quantitative trait, we mapped trans-acting loci (activity quantitative trait loci, or aQTLs) that act via posttranscriptional regulation of transcript stability. We predicted and experimentally confirmed that a coding polymorphism at the IRA2 locus modulates Puf4p activity. Our results also indicate that Puf3p activity is modulated by distinct loci, depending on whether it acts via the 5' or the 3' untranslated region of its target mRNAs. Together, our results validate a general strategy for dissecting the connectivity between posttranscriptional [corrected] regulators and their upstream signaling pathways.

Loss of subcellular lipid transport due to ARV1 deficiency disrupts organelle homeostasis and activates the unfolded protein response.

The ARV1-encoded protein mediates sterol transport from the endoplasmic reticulum (ER) to the plasma membrane. Yeast ARV1 mutants accumulate multiple lipids in the ER and are sensitive to pharmacological modulators of both sterol and sphingolipid metabolism. Using fluorescent and electron microscopy, we demonstrate sterol accumulation, subcellular membrane expansion, elevated lipid droplet formation, and vacuolar fragmentation in ARV1 mutants. Motif-based regression analysis of ARV1 deletion transcription profiles indicates activation of Hac1p, an integral component of the unfolded protein response (UPR). Accordingly, we show constitutive splicing of HAC1 transcripts, induction of a UPR reporter, and elevated expression of UPR targets in ARV1 mutants. IRE1, encoding the unfolded protein sensor in the ER lumen, exhibits a lethal genetic interaction with ARV1, indicating a viability requirement for the UPR in cells lacking ARV1. Surprisingly, ARV1 mutants expressing a variant of Ire1p defective in sensing unfolded proteins are viable. Moreover, these strains also exhibit constitutive HAC1 splicing that interacts with DTT-mediated perturbation of protein folding. These data suggest that a component of UPR induction in arv1Δ strains is distinct from protein misfolding. Decreased ARV1 expression in murine macrophages also results in UPR induction, particularly up-regulation of activating transcription factor-4, CHOP (C/EBP homologous protein), and apoptosis. Cholesterol loading or inhibition of cholesterol esterification further elevated CHOP expression in ARV1 knockdown cells. Thus, loss or down-regulation of ARV1 disturbs membrane and lipid homeostasis, resulting in a disruption of ER integrity, one consequence of which is induction of the UPR.