Biology subjects

Dickson, R. P.

Publications and source records attributed to Dickson, R. P..

9 recordsLinked to original sources

bioRxiv2025

Alveolar metabolite availability facilitates secondary infection by Pseudomonas aeruginosa in acutely injured lungs

Secondary bacterial pneumonia is a frequent complication of acute lung injury (ALI) and the acute respiratory distress syndrome (ARDS). Prior efforts to explain increased pneumonia risk in ALI/ARDS have focused on impaired immunity and bacterial virulence, but have overlooked the potential contribution of ecological factors within acutely injured lungs. Here, we show that lung injury profoundly alters the alveolar metabolic microenvironment, a change that can be exploited by a common pneumonia pathogen. In mice and humans, we found that growth of Pseudomonas aeruginosa (the most prominent respiratory pathogen in a retrospective cohort of patients with ARDS) is enhanced by increased alveolar concentrations of multiple ALI/ARDS-associated metabolites, which are derived from the blood and cross the compromised alveolar-capillary barrier during alveolar leak. Collectively, this work reveals an ecological mechanism by which pathogens may survive in the injured lung microenvironment and identifies new potential targets for secondary pneumonia prevention and treatment.

microbiology↗

bioRxiv2024

GenomicGapID: Leveraging Spatial Distribution of Conserved Genomic Sites for Broad-Spectrum Microbial Identification

Bacterial detection and identification methods can be broadly classified as either untargeted with expansive taxonomic coverage or targeted with narrow taxonomic focus. Untargeted approaches, such as culture and sequencing, are often time-consuming and/or costly, whereas targeted methods, such as PCR, can offer faster and more cost-effective results but require a priori knowledge of the likely pathogen to select the appropriate assay. GenomicGapID, a novel approach that leverages the spatial distribution of conserved genetic regions across microbial genomes, represents a significant advancement in the field of microbial identification. This technique has the potential to provide the taxonomic breadth of culture and sequencing, while maintaining the speed, simplicity, and cost-effectiveness of PCR. By leveraging the conservation and relative positioning of highly conserved coding regions across different species, GenomicGapID enables the development of universal primer sets that amplify the non-conserved gaps between these regions. This creates a unique electrophoretic signature that facilitates rapid and accurate target agnostic microbial identification. In this study, we apply the principles of GenomicGapID to the critical task of identifying clinical pathogens. We focus on expanding the coverage of a previously developed universal bacterial identification system, which initially targeted the 16s-23s internal transcribed spacer (ITS) region and was capable of discerning 45 pathogens. To enhance this system, we assembled a comprehensive database of 189 clinically relevant bacterial species. We then identified conserved primer binding sites that produce unique amplicon size signatures for each species. While we found that the use of amplicon size signatures alone would require an impractical number of universal primer sets, we demonstrate that this challenge can be effectively mitigated through concurrent melt analysis. Ultimately, we show that just three universal primer sets, guided by the GenomicGapID framework, are sufficient to cover 189 clinical bacterial pathogens, representing a majority of microbes identified in positive cultures in a clinical microbiology setting, with experimental validation of a subset of these pathogens. This study not only enhances the existing universal bacterial identification system but also establishes GenomicGapID as a versatile and powerful tool in microbial diagnostics and beyond with the potential to open new areas of investigation in genomics, with significant implications for molecular biology, clinical practice, and public health.

microbiology↗

bioRxiv2024

A rapid, inexpensive, culture-free, universal bacterial identification system using internal transcribed spacer targeting primers: a proof-of-principle study

Techniques for bacterial detection and identification can be characterized as either untargeted and taxonomically broad or targeted and taxonomically narrow. Untargeted techniques (e.g., culture and sequencing) are time-consuming and/or expensive while targeted techniques (polymerase chain reaction; PCR) can be faster and less expensive but require a strong pre-test suspicion of the potential organism to choose the correct test. We have developed a universal bacterial identification system that is as taxonomically broad as culture and amplicon sequencing but as fast, easy, and affordable as PCR. The platform utilizes a unique universal polymerase chain reaction (PCR) primer set that targets the internal transcribed spacer (ITS) regions between conserved bacterial genes, creating a distinguishable electrophoretic pattern for each bacterial species. Bioinformatic simulation demonstrates that at least 45 commonly isolated pathogenic species can be uniquely identified from a single set of PCR primers using this approach. We experimentally confirmed these predictions on seven representative human pathogens, including gram-negatives and gram-positives, aerobes and anaerobes, and spore formers. Without a priori knowledge of the organism, this system can rapidly identify the unique pattern generated by multiple species in a single reaction. Using quantitative PCR, the system can also determine the corresponding concentration of the organism in question. We also show that the primers are resilient to human DNA contamination at physiologic concentrations, eliminating the need for complex and time intensive extraction methods. Proof-of-principle testing on actual clinical specimens demonstrate that this assay can identify more than twice the species as current multiplex PCR assays (e.g., BioFire) using only one universal primer pair in a single PCR reaction, providing results in <3 hours for <$20 without bioinformatic turnaround time. SIGNIFICANCE STATEMENTThis paper describes a novel bacterial identification system that is as taxonomically broad as culture and amplicon sequencing but as fast, easy, and affordable as PCR. The assay covers more than twice the species as the leading multiplex PCR assays but uses only one universal primer pair in a single PCR reaction. It is resilient to human genomic contamination precluding the need for timely or costly methods to clear human DNA. This approach represents a major advancement in the decades-long struggle to rapidly and accurately identify bacteria.

bioengineering↗

bioRxiv2022

Rapid Real-time Squiggle Classification for Read Until Using RawMap

ReadUntil enables Oxford Nanopore Technologys (ONT) sequencers to selectively sequence reads of target species in real-time. This enables efficient microbial enrichment for applications such as microbial abundance estimation and is particularly beneficial for metagenomic samples with a very high fraction of non-target reads (> 99% can be human reads). However, read-until requires a fast and accurate software filter that analyzes a short prefix of a read and determines if it belongs to a microbe of interest (target) or not. The baseline Read Until pipeline uses a deep neural network-based basecaller called Guppy and is slow and inaccurate for this task ([~]60% of bases sequenced are unclassified). We present RawMap, an efficient CPU-only microbial species-agnostic Read Until classifier for filtering non-target human reads in the squiggle space. RawMap uses a Support Vector Machine (SVM), which is trained to distinguish human from microbe using non-linear and non-stationary characteristics of ONTs squiggle output (continuous electrical signals). Compared to the baseline Read Until pipeline, RawMap is a 1327X faster classifier and significantly improves the sequencing time and cost, and compute time savings. We show that RawMap augmented pipelines reduce sequencing time and cost by [~]24% and computing cost by [~]22%. Additionally, since RawMap is agnostic to microbial species, it can also classify microbial species it is not trained on. We also discuss how RawMap may be used as an alternative to the RT-PCR test for viral load quantification of SARS-CoV-2. Availability and implementationSoftware is released with MIT License and available on GitHub: https://github.com/harisankarsadasivan/RawMap

bioinformatics↗

bioRxiv2021

Ultra-rapid somatic variant detection via real-time threshold sequencing

Molecular markers are becoming increasingly important for cancer diagnosis, proper clinical trial enrollment, and even surgical decision making, motivating ultra-rapid, intraoperative variant detection. Sequencing-based detection is considered the gold standard approach, but typically takes hours to perform. In this work, we present Threshold Sequencing, a methodology for designing protocols for targeted variant detection on real-time sequencers with a minimal time to result. Threshold Sequencing analytically identifies a time-optimal threshold to stop target amplification and begin sequencing. To further reduce diagnostic time, we explore targeted Loop-mediated Isothermal Amplification (LAMP) and design a LAMP-specific bioinformatics tool--LAMPrey--to process sequenced LAMP product. LAMPreys concatemer aware alignment algorithm is designed to maximize recovery of diagnostically relevant information leading to a more rapid detection versus standard read alignment approaches. Coupled with time-optimized DNA extraction and library preparation, we demonstrate confirmation of a hot-spot mutation (250x support) from tumor tissue in less than 30 minutes.

bioinformatics↗

bioRxiv2021

A Novel Swine Model of the Acute Respiratory Distress Syndrome Using Clinically-Relevant Injury Exposures

To date, existing animal models of the acute respiratory distress syndrome (ARDS) have failed to translate preclinical discoveries into effective pharmacotherapy or diagnostic biomarkers. To address this translational gap, we developed a high-fidelity swine model of ARDS utilizing clinically-relevant lung injury exposures. Fourteen male swine were anesthetized, mechanically ventilated, and surgically instrumented for hemodynamic monitoring, blood, and tissue sampling. Animals were allocated to one of three groups: 1) Indirect lung injury only: animals were inoculated by direct injection of E. coli into the kidney parenchyma, provoking systemic inflammation and distributive shock physiology; 2) Direct lung injury only: animals received volutrauma, hyperoxia, and bronchoscope-delivered gastric particles; 3) Combined indirect and direct lung injury: animals were administered both above-described indirect and direct lung injury exposures. Animals were monitored for up to 12 hours, with serial collection of physiologic data, blood samples, and radiographic imaging. Lung tissue was acquired post-mortem for pathological examination. In contrast to indirect lung injury only and direct lung injury only groups, animals in the combined indirect and direct lung injury group exhibited all of the physiological, radiographic, and histopathologic hallmarks of human ARDS: impaired gas exchange (mean PaO2/FiO2 ratio 124.8 {+/-} 63.8), diffuse bilateral opacities on chest radiographs, and extensive pathologic evidence of diffuse alveolar damage. Our novel porcine model of ARDS, built on clinically-relevant lung injury exposures, faithfully recapitulates the physiologic, radiographic, and histopathologic features of human ARDS, and fills a crucial gap in the translational study of human lung injury.

pathology↗

bioRxiv2021

Real-Time, Direct Classification of Nanopore Signals with SquiggleNet

Oxford Nanopore sequencers provide results in real time as DNA passes through a nanopore and can eject a molecule after it has been partly sequenced. However, the computational challenge of deciding whether to keep or reject a molecule in real time has limited the application of this capability. We present SquiggleNet, the first deep learning model that can classify nanopore reads directly from their electrical signals. SquiggleNet operates faster than the DNA passes through the pore, allowing real-time classification and read ejection. When given the amount of sequencing data generated in one second, the classifier achieves significantly higher accuracy than base calling followed by sequence alignment. Our approach is also faster and requires an order of magnitude less memory than approaches based on alignment. SquiggleNet distinguished human from bacterial DNA with over 90% accuracy, generalized to unseen species, identified bacterial species in a human respiratory meta genome sample, and accurately classified sequences containing human long interspersed repeat elements.

bioinformatics↗

bioRxiv2020

Application of an ecology-based analytic approach to discriminate signal and noise in low-biomass microbiome studies: whole lung tissue is the preferred sampling method for amplicon-based characterization of murine lung microbiota

BackgroundLow-biomass microbiome studies (such as those of the lungs, placenta, and skin) are vulnerable to contamination and sequencing stochasticity, which obscure legitimate microbial signal. Since low-biomass microbiome fields have had variable success in establishing the reality and clinical significance of identified microbiota, we sought to develop and apply an analytical approach to discriminate signal from noise in low-biomass microbiome studies. We used this approach to determine the optimal sampling strategy in murine lung microbiome studies, which will be essential for future mechanistic lung microbiome research. MethodsUsing a novel, ecology-based analytic approach, we compared bacterial DNA from the lungs of healthy adult mice collected via two common sampling approaches: homogenized whole lung tissue and bronchoalveolar lavage (BAL) fluid. We quantified bacterial DNA using droplet digital PCR, characterized bacterial communities using 16S rRNA gene sequencing, and systematically assessed the quantity and identity of bacterial DNA in both specimen types. We compared bacteria detected in lung specimens to each other and to potential source communities: negative (background) control specimens and paired oral samples. FindingsBy all measures, whole lung tissue in mice contained greater bacterial signal and less evidence of contamination than did BAL fluid. Relative to BAL fluid, whole lung tissue exhibited a greater quantity of bacterial DNA, distinct community composition, decreased sample-to-sample variation, and greater biological plausibility when compared to potential source communities. In contrast, bacteria detected in BAL fluid were minimally different from those of procedural, reagent, and sequencing controls. InterpretationAn ecology-based analytical approach discriminates signal from noise in low-biomass microbiome studies and identifies whole lung tissue as the preferred specimen type for murine lung microbiome studies. Sequencing, analysis, and reporting of potential source communities, including negative control specimens and contiguous biological sites, is crucial for biological interpretation of low-biomass microbiome studies, independent of specimen type. FundingNational Institutes of Health

microbiology↗

bioRxiv2019

Rapid breath analysis for acute respiratory distress syndrome diagnostics using a portable 2-dimensional gas chromatography device

Acute respiratory distress syndrome (ARDS) is the most severe form of acute lung injury, responsible for high mortality and long-term morbidity. As a dynamic syndrome with multiple etiologies its timely diagnosis is difficult as is tracking the course of the syndrome. Therefore, there is a significant need for early, rapid detection and diagnosis as well as clinical trajectory monitoring of ARDS. Here we report our work on using human breath to differentiate ARDS and non-ARDS causes of respiratory failure. A fully automated portable 2-dimensional gas chromatography device with high peak capacity, high sensitivity, and rapid analysis capability was designed and made in-house for on-site analysis of patients breath. A total of 85 breath samples from 48 ARDS patients and controls were collected. Ninety-seven elution peaks were separated and detected in 13 minutes. An algorithm based on machine learning, principal component analysis (PCA), and linear discriminant analysis (LDA) was developed. As compared to the adjudications done by physicians based on the Berlin criteria, our device and algorithm achieved an overall accuracy of 87.1% with 94.1% positive predictive value and 82.4% negative predictive value. The high overall accuracy and high positive predicative value suggest that the breath analysis method can accurately diagnose ARDS. The ability to continuously and non-invasively monitor exhaled breath for early diagnosis, disease trajectory tracking, and outcome prediction monitoring of ARDS may have a significant impact on changing practice and improving patient outcomes.

bioengineering↗