Biology subjects

Hogan, D. A.

Publications and source records attributed to Hogan, D. A..

5 recordsLinked to original sources

bioRxiv2018

Ethanol decreases Pseudomonas aeruginosa flagellar motility through a cyclic-di-GMP- and stator-dependent pathway

Pseudomonas aeruginosa frequently encounters microbes that produce bioactive metabolites including ethanol. At concentrations that do not affect growth, we found that ethanol reduces P. aeruginosa motility by 30% in a swim agar assay and this decrease is accompanied by a 2.5-fold increase in levels of cyclic diguanylate (c-di-GMP), a second messenger that represses motility, in planktonic cells. A screen of mutants lacking genes involved in c-di-GMP metabolism identified SadC and GcbA as diguanylate cyclases involved in swim zone reduction by ethanol and ethanol-induced c-di-GMP production. The reduction of swimming in response to ethanol also required the stator set, MotAB, two PilZ-domain proteins (FlgZ and PilZ), PilY1-a proposed surface-sensing protein, and PilMNOP, which comprises the pilus alignment complex and these proteins have been previously implicated in the control of motility on agar surfaces. Microscopic analysis of the fraction of quiescent cells in swim medium showed that ethanol decreased the portion of motile cells in the wild type, but had opposite effects in the {triangleup}pilY1, {triangleup}pilMNOP, {triangleup}motAB, and {triangleup}pilZ{triangleup}flgZ mutants. Together, these data indicate ethanol induces a regulated change in motility in planktonic cells at concentrations similar to those produced by other microbes. We propose that this ethanol-responsiveness may contribute to the co-localization of P. aeruginosa with ethanol-producing microbes.

microbiology↗

bioRxiv2018

Refining the application of microbial lipids as tracers of Staphylococcus aureus growth rates in cystic fibrosis sputum

Chronic lung infections in cystic fibrosis (CF) could be treated more effectively if the effect of antimicrobials on pathogens in situ were known. Here, we compared changes in the microbial community composition and pathogen growth rates in longitudinal studies of CF patients undergoing intravenous antibiotic administration during pulmonary exacerbations. Microbial community composition was measured by NanoString DNA analysis and growth rates were obtained by incubating CF sputum with heavy water and tracing incorporation of deuterium into two different anteiso fatty acids (a-C15:0 and a-C17:0) using gas chromatography-mass spectrometry (GC/MS). Prior to this study, both lipids were thought to be specific for Staphylococcaceae and hence their isotopic enrichment was interpreted as a growth proxy for S. aureus. Our experiments revealed, however, that Prevotella is also a relevant microbial producer of a-C17:0 fatty acid in some CF patients, thus deuterium incorporation into these lipids is better interpreted as a more general pathogen growth rate proxy. Even accounting for a small non-microbial background source detected in some patient samples, a-C15:0 fatty acid still appear to be a relatively robust proxy for CF pathogens, revealing a median generation time of ~1.5 days, similar to prior observations. Contrary to our expectations, pathogen growth rates remained relatively stable throughout exacerbation treatment. We suggest two best practices for application of stable isotope probing in CF sputum: (1) parallel determination of microbial community composition in CF sputum using culture-independent tools, and (2) analysis of samples with a minimum a-C15:0 concentration of 0.1 weight percent of saturated fatty acids.\n\nIMPORTANCEIn chronic lung infections, populations of microbial pathogens change and mature in ways that are often unknown, which makes it challenging to identify appropriate treatment options. A promising tool to better understand the physiology of microorganisms in a patient is stable-isotope probing, which we previously developed to estimate the growth rates of S. aureus in cystic fibrosis (CF) sputum. Here, we tracked microbial communities in a cohort of CF patients and found that anteiso fatty acids can also originate from other sources in CF sputum. This awareness led us to develop an new workflow for the application of stable isotope probing in this context, improving our ability to estimate pathogen generation times in clinical samples.

microbiology↗

bioRxiv2018

Profiling of bacterial and fungal microbial communities in cystic fibrosis sputum using RNA

Here, we report an approach to detect diverse bacterial and fungal taxa in complex samples by direct analysis of community RNA in one step using NanoString probe sets. We designed rRNA-targeting probe sets to detect forty two bacterial and fungal genera or species common in cystic fibrosis (CF) sputum, and demonstrated taxon-specificity of these probes as well as a linear response over more than three logs of input RNA. Culture-based analyses correlated qualitatively with relative abundance data on bacterial and fungal taxa obtained by NanoString and the analysis of serial samples demonstrated the use of this method to simultaneously detect bacteria and fungi and to detect microbes at low abundance without an amplification step. The relative abundances of bacterial taxa detected by analysis of RNA correlated with the relative abundances of the same taxa as measured by sequencing of the V4V5 region of the 16S rRNA gene amplified from community DNA from the same sample. We propose that this method may complement other methods designed to understand dynamic microbial communities, may provide information on bacteria and fungi in the same sample with a single assay, and, with further development, may provide quick and easily-interpreted diagnostic information on diverse bacteria and fungi at the genus or species level.\n\nImportanceHere we demonstrate the use of an RNA-based analysis of specific taxa of interest, including bacteria and fungi, within microbial communities. This multiplex method may be useful as a means to identify samples with specific combinations of taxa and to gain information on how specific populations vary over time and space or in response to perturbation. A rapid means to measure bacterial and fungal populations may aid in the study of host response to changes in microbial communities.

microbiology↗

bioRxiv2017

ADAGE signature analysis: differential expression analysis with data-defined gene sets

BackgroundGene set enrichment analysis and overrepresentation analyses are commonly used methods to determine the biological processes affected by a differential expression experiment. This approach requires biologically relevant gene sets, which are currently curated manually, limiting their availability and accuracy in many organisms without extensively curated resources. New feature learning approaches can now be paired with existing data collections to directly extract functional gene sets from big data.\n\nResultsHere we introduce a method to identify perturbed processes. In contrast with methods that use curated gene sets, this approach uses signatures extracted from public expression data. We first extract expression signatures from public data using ADAGE, a neural network-based feature extraction approach. We next identify signatures that are differentially active under a given treatment. Our results demonstrate that these signatures represent biological processes that are perturbed by the experiment. Because these signatures are directly learned from data without supervision, they can identify uncurated or novel biological processes. We implemented ADAGE signature analysis for the bacterial pathogen Pseudomonas aeruginosa. For the convenience of different user groups, we implemented both an R package (ADAGEpath) and a web server (http://adage.greenelab.com) to run these analyses. Both are open-source to allow easy expansion to other organisms or signature generation methods. We applied ADAGE signature analysis to an example dataset in which wild-type and{Delta} anr mutant cells were grown as biofilms on the Cystic Fibrosis genotype bronchial epithelial cells. We mapped active signatures in the dataset to KEGG pathways and compared with pathways identified using GSEA. The two approaches generally return consistent results; however, ADAGE signature analysis also identified a signature that revealed the molecularly supported link between the MexT regulon and Anr.\n\nConclusionsWe designed ADAGE signature analysis to perform gene set analysis using data-defined functional gene signatures. This approach addresses an important gap for biologists studying non-traditional model organisms and those without extensive curated resources available. We built both an R package and web server to provide ADAGE signature analysis to the community.

bioinformatics↗

bioRxiv2017

PathCORE: Visualizing globally co-occurring pathways in large transcriptomic compendia

BackgroundInvestigators often interpret genome-wide data by analyzing the expression levels of genes within pathways. While this within-pathway analysis is routine, the products of any one pathway can affect the activity of other pathways. Past efforts to identify relationships between biological processes have evaluated overlap in knowledge bases or evaluated changes that occur after specific treatments. Individual experiments can highlight condition-specific pathway-pathway relationships; however, constructing a complete network of such relationships across many conditions requires analyzing results from many studies. ResultsWe developed PathCORE-T framework by implementing existing methods to identify pathway-pathway transcriptional relationships evident across a broad data compendium. PathCORE-T is applied to the output of feature construction algorithms; it identifies pairs of pathways observed in features more than expected by chance as functionally co-occurring. We demonstrate PathCORE-T by analyzing an existing eADAGE model of a microbial compendium and building and analyzing NMF features from the TCGA dataset of 33 cancer types. The PathCORE-T framework includes a demonstration web interface, with source code, that users can launch to (1) visualize the network and (2) review the expression levels of associated genes in the original data. PathCORE-T creates and displays the network of globally co-occurring pathways based on features observed in a machine learning analysis of gene expression data. ConclusionsThe PathCORE-T framework identifies transcriptionally co-occurring pathways from the results of unsupervised analysis of gene expression data and visualizes the relationships between pathways as a network. PathCORE-T recapitulated previously described pathway-pathway relationships and suggested experimentally testable additional hypotheses that remain to be explored.

bioinformatics↗