Biology subjects

Neff, S. L.

Publications and source records attributed to Neff, S. L..

6 recordsLinked to original sources

bioRxiv2024

E.PathDash, pathway activation analysis of public cystic fibrosis pathogen gene expression data

E.PathDash facilitates re-analysis of gene expression data from pathogens clinically relevant to chronic respiratory diseases, including a total of 48 studies, 548 samples, and 404 unique treatment comparisons. The application enables users to assess broad biological stress responses at the KEGG pathway or Gene Ontology level and also provides data for individual genes. E.PathDash reduces the time required to gain access to data from multiple hours per dataset to seconds. Users can download high quality images such as volcano plots and boxplots, differential gene expression results and raw count data, making it fully interoperable with other tools. Importantly, users can rapidly toggle between experimental comparisons and different studies of the same phenomenon, enabling them to judge the extent to which observed responses are reproducible. As a proof of principle, we invited two cystic fibrosis scientists to use the application to explore scientific questions relevant to their specific research areas. Reassuringly, pathway activation analysis recapitulated results reported in original publications, but it also yielded new insights into pathogen responses to changes in their environments, validating the utility of the application. All software and data are freely accessible and the application is available at scangeo.dartmouth.edu/EPathDash. ImportanceChronic respiratory illnesses impose a high disease burden on our communities and people with respiratory diseases are susceptible to robust bacterial infections from pathogens, including Pseudomonas aeruginosa and Staphylococcus aureus, that contribute to morbidity and mortality. Public gene expression datasets generated from these and other pathogens are abundantly available and an important resource for synthesizing existing pathogenic research, leading to interventions that improve patient outcomes. However, it can take many hours or weeks to render publicly available datasets usable; significant time and skills are needed to clean, standardize, and apply reproducible and robust bioinformatic pipelines to the data. Through collaboration with two microbiologists we have shown that E.PathDash addresses this problem, enabling them to elucidate pathogen responses to a variety of over 400 experimental conditions and generate mechanistic hypotheses for cell-level behavior in response to disease-relevant exposures, all in a fraction of the time.

bioinformatics↗

bioRxiv2023

Analysis of Pseudomonas aeruginosa transcription in an ex vivo cystic fibrosis sputum model identifies metal restriction as a gene expression stimulus

Chronic Pseudomonas aeruginosa lung infections are a distinctive feature of cystic fibrosis (CF) pathology, that challenge adults with CF even with the advent of highly effective modulator therapies. Characterizing P. aeruginosa transcription in the CF lung and identifying factors that drive gene expression could yield novel strategies to eradicate infection or otherwise improve outcomes. To complement published P. aeruginosa gene expression studies in laboratory culture models designed to model the CF lung environment, we employed an ex vivo sputum model in which laboratory strain PAO1 was incubated in sputum from different CF donors. As part of the analysis, we compared PAO1 gene expression in this "spike-in" sputum model to that for P. aeruginosa grown in artificial sputum medium (ASM). Analyses focused on genes that were differentially expressed between sputum and ASM and genes that were most highly expressed in sputum. We present a new approach that used sets of genes with correlated expression, identified by the gene expression analysis tool eADAGE, to analyze the differential activity of pathways in P. aeruginosa grown in CF sputum from different individuals. A key characteristic of P. aeruginosa grown in expectorated CF sputum was related to zinc and iron acquisition, but this signal varied by donor sputum. In addition, a significant correlation between P. aeruginosa expression of the H1-type VI secretion system and corrector use by the sputum donor was observed. These methods may be broadly useful in looking for variable signals across clinical samples. ImportanceIdentifying the gene expression programs used by Pseudomonas aeruginosa to colonize the lungs of people with cystic fibrosis (CF) will illuminate new therapeutic strategies. To capture these transcriptional programs, we cultured the common P. aeruginosa laboratory strain PAO1 in expectorated sputum from CF patient donors. Through bioinformatics analysis, we defined sets of genes that are more transcriptionally active in real CF sputum compared to artificial sputum media (ASM). Many of the most differentially active gene sets contained genes related to metal acquisition, suggesting that these gene sets play an active role in scavenging for metals in the CF lung environment which is inadequately represented in ASM. Future studies of P. aeruginosa transcription in CF may benefit from the use of an expectorated sputum model or modified forms of ASM supplemented with metals.

microbiology↗

bioRxiv2023

Rocket-miR, a Translational Launchpad for miRNA-based Antimicrobial Drug Development

Developing software tools that leverage biological datasets to accelerate drug discovery is an important aspect of bioinformatics research. Here we present a novel example: a web application called Rocket-miR that applies an existing bioinformatics algorithm (IntaRNA) to predict cross-species miRNA-mRNA interactions and identify human miRNAs with potential antimicrobial activity against antibiotic resistant bacterial infections. Rocket-miR is the logical extension of our prior finding that human miRNA let-7b-5p impairs the ability of the ubiquitous opportunistic pathogen P. aeruginosa to form biofilms and resist the bactericidal effect of beta lactam antibiotics. Rocket-miRs point and click interface enables researchers without programming expertise to predict additional human-miRNA-pathogen interactions. Identified miRNAs can be developed into novel antimicrobials effective against the 24 clinically relevant pathogens, implicated in diseases of the lung, gut and other organs, that are included in the application. The manuscript incorporates three case studies contributed by microbiologists that study human pathogens to demonstrate the usefulness and usability of the application. Rocket-miR is accessible at the following link: http://scangeo.dartmouth.edu/RocketmiR/. ImportanceAntimicrobial resistant infections contribute to millions of deaths worldwide every year. In particular, the group of bacteria collectively known as ESKAPE (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa and Enterobacter sp.) pathogens are of considerable medical concern due to their virulence and exceptional ability to develop antibiotic resistance. New kinds of antimicrobial therapies are urgently needed to treat patients for whom existing antibiotics are ineffective. The Rocket-miR application predicts targets of human miRNAs in bacterial and fungal pathogens, rapidly identifying candidate miRNA-based antimicrobials. The applications target audience is microbiologists that have the laboratory resources to test the applications predictions. The Rocket-miR application currently supports 24 recognized human pathogens that are relevant to numerous diseases including CF, COPD, urinary tract infections and pneumonia. Furthermore, the application code was designed to be easily extendible to other human pathogens that commonly cause hospital-acquired infections.

bioinformatics↗

bioRxiv2022

Compendium-wide analysis of P. aeruginosa core and accessory genes reveal more nuanced transcriptional patterns

Strains of Pseudomonas aeruginosa, an opportunistic pathogen that causes difficult to treat infections, have significant genomic heterogeneity including the presence of diverse accessory genes that are only present in some strains or clades. Both core genes, which are conserved across strains, and accessory genes have been associated with traits such as biofilm formation and virulence. Much of what we know about core and accessory gene content comes from genome analyses. Here, we use a newly assembled transcriptome compendium to analyze the transcriptional patterns of core and accessory gene expression in PAO1 and PA14 strains across thousands of samples from hundreds of distinct experiments. We found that a subset of core genes were stable, having consistent correlated expression patterns across samples regardless of strain background, with a focus on strains PAO1 and PA14. These stable core genes had fewer co-expressed neighbors that were accessory genes. SignificancePseudomonas aeruginosa is a ubiquitous pathogen. There is a lot of diversity amongst P. aeruginosa strains, some which are clinically relevant. Understanding how these different strain-level traits manifest is important for identifying targets that regulate different traits of interest. With the availability of a PAO1-mapped and PA14-mapped RNA-seq compendium, which contain hundreds of strains, it is now possible to examine the effect of different strains on expression, which can mediate different traits. In this study we developed an approach to compare expression profiles across different P. aeruginosa gene groups - core and accessory genes. This approach revealed a subset of core genes with different transcriptional patterns across strains, which could contribute to trait differences.

bioinformatics↗

bioRxiv2022

CF-Seq, An Accessible Web Application for Rapid Re-Analysis of Cystic Fibrosis Pathogen RNA Sequencing Studies

Researchers studying cystic fibrosis (CF) pathogens have produced numerous RNA-seq datasets which are available in the gene expression omnibus (GEO). Although these studies are publicly available, substantial computational expertise and manual effort are required to compare similar studies, visualize gene expression patterns within studies, and use published data to generate new experimental hypotheses. Furthermore, it is difficult to filter available studies by domain-relevant attributes such as strain, treatment, or media, or for a researcher to assess how a specific gene responds to various experimental conditions across studies. To reduce these barriers to data re-analysis, we have developed an R Shiny application called CF-Seq, which works with a compendium of 147 studies and 1,446 individual samples from 13 clinically relevant CF pathogens. The application allows users to filter studies by experimental factors and to view complex differential gene expression analyses at the click of a button. Here we present a series of use cases that demonstrate the application is a useful and efficient tool for new hypothesis generation. (CFSeq: http://scangeo.dartmouth.edu/CFSeq/)

bioinformatics↗

bioRxiv2022

Computationally efficient assembly of a Pseudomonas aeruginosa gene expression compendium

Over the past two decades, thousands of RNA sequencing (RNA-seq) gene expression profiles of Pseudomonas aeruginosa have been made publicly available via the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA). In the work we present here, we draw on over 2,300 P. aeruginosa transcriptomes from hundreds of studies performed by over seventy-five different research groups. We first developed a pipeline, using the Salmon pseudo-aligner and two different P. aeruginosa reference genomes (strains PAO1 and PA14), that transformed raw sequence data into a uniformly processed data in the form of sample-wise normalized counts. In this workflow, P. aeruginosa RNA-seq data are filtered using technically and biologically driven criteria with characteristics tailored to bacterial gene expression and that account for the effects of alignment to different reference genomes. The filtered data are then normalized to enable cross experiment comparisons. Finally, annotations are programmatically collected for those samples with sufficient meta-data and expression-based metrics are used to further enhance strain assignment for each sample. Our processing and quality control methods provide a scalable framework for taking full advantage of the troves of biological information hibernating in the depths of microbial gene expression data. The re-analysis of these data in aggregate is a powerful approach for hypothesis generation and testing, and this approach can be applied to transcriptome datasets in other species. SignificancePseudomonas aeruginosa causes a wide range of infections including chronic infections associated with cystic fibrosis. P. aeruginosa infections are difficult to treat and people with CF-associated P. aeruginosa infections often have poor clinical outcomes. To aid the study of this important pathogen, we developed a methodology that facilitates analyses across experiments, strains, and conditions. We aligned, filtered for quality and normalized thousands of P. aeruginosa RNA-seq gene expression profiles that were publicly available via the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA). The workflow that we present can be efficiently scaled to incorporate new data and applied to the analysis of other species.

microbiology↗