A novel clinical metaproteomics workflow enables bioinformatic analysis of host-microbe dynamics in disease

Clinical metaproteomics has the potential to offer insights into the host-microbiome interactions underlying diseases. However, the field faces challenges in characterizing microbial proteins found in clinical samples, which are usually present at low abundance relative to the host proteins. As a solution, we have developed an integrated workflow coupling mass spectrometry-based analysis with customized bioinformatic identification, quantification and prioritization of microbial and host proteins, enabling targeted assay development to investigate host-microbe dynamics in disease. The bioinformatics tools are implemented in the Galaxy ecosystem, offering the development and dissemination of complex bioinformatic workflows. The modular workflow integrates MetaNovo (to generate a reduced protein database), SearchGUI/PeptideShaker and MaxQuant (to generate peptide-spectral matches (PSMs) and quantification), PepQuery2 (to verify the quality of PSMs), and Unipept and MSstatsTMT (for taxonomy and functional annotation). We have utilized this workflow in diverse clinical samples, from the characterization of nasopharyngeal swab samples to bronchoalveolar lavage fluid. Here, we demonstrate its effectiveness via analysis of residual fluid from cervical swabs. The complete workflow, including training data and documentation, is available via the Galaxy Training Network, empowering non-expert researchers to utilize these powerful tools in their clinical studies.

Integrative meta-omics in Galaxy and beyond

BACKGROUND

'Omics methods have empowered scientists to tackle the complexity of microbial communities on a scale not attainable before. Individually, omics analyses can provide great insight; while combined as "meta-omics", they enhance the understanding of which organisms occupy specific metabolic niches, how they interact, and how they utilize environmental nutrients. Here we present three integrative meta-omics workflows, developed in Galaxy, for enhanced analysis and integration of metagenomics, metatranscriptomics, and metaproteomics, combined with our newly developed web-application, ViMO (Visualizer for Meta-Omics) to analyse metabolisms in complex microbial communities.

RESULTS

In this study, we applied the workflows on a highly efficient cellulose-degrading minimal consortium enriched from a biogas reactor to analyse the key roles of uncultured microorganisms in complex biomass degradation processes. Metagenomic analysis recovered metagenome-assembled genomes (MAGs) for several constituent populations including Hungateiclostridium thermocellum, Thermoclostridium stercorarium and multiple heterogenic strains affiliated to Coprothermobacter proteolyticus. The metagenomics workflow was developed as two modules, one standard, and one optimized for improving the MAG quality in complex samples by implementing a combination of single- and co-assembly, and dereplication after binning. The exploration of the active pathways within the recovered MAGs can be visualized in ViMO, which also provides an overview of the MAG taxonomy and quality (contamination and completeness), and information about carbohydrate-active enzymes (CAZymes), as well as KEGG annotations and pathways, with counts and abundances at both mRNA and protein level. To achieve this, the metatranscriptomic reads and metaproteomic mass-spectrometry spectra are mapped onto predicted genes from the metagenome to analyse the functional potential of MAGs, as well as the actual expressed proteins and functions of the microbiome, all visualized in ViMO.

CONCLUSION

Our three workflows for integrative meta-omics in combination with ViMO presents a progression in the analysis of 'omics data, particularly within Galaxy, but also beyond. The optimized metagenomics workflow allows for detailed reconstruction of microbial community consisting of MAGs with high quality, and thus improves analyses of the metabolism of the microbiome, using the metatranscriptomics and metaproteomics workflows.

A Galaxy of informatics resources for MS-based proteomics

INTRODUCTION

Continuous advances in mass spectrometry (MS) technologies have enabled deeper and more reproducible proteome characterization and a better understanding of biological systems when integrated with other 'omics data. Bioinformatic resources meeting the analysis requirements of increasingly complex MS-based proteomic data and associated multi-omic data are critically needed. These requirements included availability of software that would span diverse types of analyses, scalability for large-scale, compute-intensive applications, and mechanisms to ease adoption of the software.

AREAS COVERED

The Galaxy ecosystem meets these requirements by offering a multitude of open-source tools for MS-based proteomics analyses and applications, all in an adaptable, scalable, and accessible computing environment. A thriving global community maintains these software and associated training resources to empower researcher-driven analyses.

EXPERT OPINION

The community-supported Galaxy ecosystem remains a crucial contributor to basic biological and clinical studies using MS-based proteomics. In addition to the current status of Galaxy-based resources, we describe ongoing developments for meeting emerging challenges in MS-based proteomic informatics. We hope this review will catalyze increased use of Galaxy by researchers employing MS-based proteomics and inspire software developers to join the community and implement new tools, workflows, and associated training content that will add further value to this already rich ecosystem.

The impact of incorporating surgical simulation into trichiasis surgery training on operative aspects of initial live-training surgeries

BACKGROUND

While surgical simulation is regularly used in surgical training in high-income country settings, it is uncommon in low- and middle-income countries, particularly for surgical training that primarily occurs in rural areas. We designed and evaluated a novel surgical simulator for improving trachomatous trichiasis (TT) surgery training, given that trichiasis is mostly found among the poorest individuals in rural areas.

METHODOLOGY/PRINCIPAL FINDINGS

TT surgery programs were invited to incorporate surgical simulation with a new, high fidelity, low-cost simulator into their training. Trainees completed standard TT-surgery training following World Health Organization guidelines. A subset of trainees received three hours of supplemental training with the simulator between classroom and live-surgery training. We recorded the time required to complete each surgery and the number of times the trainer intervened to correct surgical steps. Participants completed questionnaires regarding their perceptions. We also assessed trainer and trainee perceptions of surgical simulation training as part of trichiasis surgery training. 22 surgeons completed standard training and 26 completed standard training plus simulation. We observed 1,394 live-training surgeries. Average time to first live-training surgery completion was nearly 20% shorter the simulation versus the standard group (28.3 vs 34.4 minutes; p = 0.02). Trainers intervened significantly fewer times during initial live-training surgeries in the simulation group (2.7 vs. 4.8; p = 0.005). All trainers indicated the simulator significantly improved training by allowing trainees to practice safely and to identify problem areas before performing live-training surgeries. Trainees reported that simulation practice improved their confidence and skills prior to performing live-training surgeries.

CONCLUSIONS

A single high-fidelity surgical simulation session can significantly improve critical aspects of initial TT surgeries.

Catching the Wave: Detecting Strain-Specific SARS-CoV-2 Peptides in Clinical Samples Collected during Infection Waves from Diverse Geographical Locations

The Coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) resulted in a major health crisis worldwide with its continuously emerging new strains, resulting in new viral variants that drive "waves" of infection. PCR or antigen detection assays have been routinely used to detect clinical infections; however, the emergence of these newer strains has presented challenges in detection. One of the alternatives has been to detect and characterize variant-specific peptide sequences from viral proteins using mass spectrometry (MS)-based methods. MS methods can potentially help in both diagnostics and vaccine development by understanding the dynamic changes in the viral proteome associated with specific strains and infection waves. In this study, we developed an accessible, flexible, and shareable bioinformatics workflow that was implemented in the Galaxy Platform to detect variant-specific peptide sequences from MS data derived from the clinical samples. We demonstrated the utility of the workflow by characterizing published clinical data from across the world during various pandemic waves. Our analysis identified six SARS-CoV-2 variant-specific peptides suitable for confident detection by MS in commonly collected clinical samples.

VaxArray immunoassay for the multiplexed quantification of poliovirus D-antigen

Next generation poliovirus vaccines are critical to reaching global poliovirus eradication goals. Recent efforts have focused on creating inactivated vaccines using attenuated Sabin strains that maintain patient safety benefits and immunogenicity of conventional inactivated vaccines while increasing manufacturing safety and lowering production costs, and on developing novel oral vaccines using modified Sabin strains that provide critical mucosal immunity but are further attenuated to minimize risk of reversion to neurovirulence. In addition, there is a push to improve the analytical tools for poliovirus vaccine characterization. Conventional and Sabin inactivated poliovirus vaccines typically rely on standard plate-based ELISA as in vitro D-antigen potency assays in combination with WHO international standards as calibrants. While widely utilized, the current D-antigen ELISA assays have a long time to result (up to 72 h), can suffer from lab-to-lab inconsistency due to non-standardized protocols and reagents, and are inherently singleplex. For D-antigen quantitation, we have developed the VaxArray Polio Assay Kit, a multiplexed, microarray-based immunoassay that uses poliovirus-specific human monoclonal antibodies currently under consideration as standardized reagents for characterizing inactivated Sabin and Salk vaccines. The VaxArray assay can simultaneously quantify all 3 poliovirus serotypes with a time to result of less than 3 h. Here we demonstrate that the assay has limits of quantification suitable for both bioprocess samples and final vaccines, excellent reproducibility and precision, and improved accuracy over an analogous plate-based ELISA. The assay is suitable for adjuvanted combination vaccines, as common vaccine additives and crude matrices do not interfere with quantification, and is intended as a high throughput, standardized quantitation tool to aid inactivated poliovirus vaccine manufacturers in streamlining vaccine development and manufacturing, aiding the global polio eradication effort.

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Multiplexed D-antigen immunoassay for all 3 poliovirus serotypes

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Has <3 h time to result and compares well to 3-day plate-based ELISA

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Assay shows high specificity and is reactive to sIPV, cIPV, and OPV

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Applicable to in-process samples, final IPV and combination vaccine formulations

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High accuracy and precision for both sIPV and cIPV over multiple users and days

Refractory and Super-Refractory Status Epilepticus in Nerve Agent-Poisoned Rats Following Application of Standard Clinical Treatment Guidelines

Nerve agents (NAs) induce a severe cholinergic crisis that can lead to status epilepticus (SE). Current guidelines for treatment of NA-induced SE only include prehospital benzodiazepines, which may not fully resolve this life-threatening condition. This study examined the efficacy of general clinical protocols for treatment of SE in the specific context of NA poisoning in adult male rats. Treatment with both intramuscular and intravenous benzodiazepines was entirely insufficient to control SE. Second line intervention with valproate (VPA) initially terminated SE in 35% of rats, but seizures always returned. Phenobarbital (PHB) was more effective, with SE terminating in 56% of rats and 19% of rats remaining seizure-free for at least 24 h. The majority of rats demonstrated refractory SE (RSE) and required treatment with a continuous third-line anesthetic. Both ketamine (KET) and propofol (PRO) led to high levels of mortality, and nearly all rats on these therapies had breakthrough seizure activity, demonstrating super-refractory SE (SRSE). For the small subset of rats in which SE was fully resolved, significant improvements over controls were observed in recovery metrics, behavioral assays, and brain pathology. Together these data suggest that NA-induced SE is particularly severe, but aggressive treatment in the intensive care setting can lead to positive functional outcomes for casualties.

The role of Cytochrome b6f in the control of steady-state photosynthesis: a conceptual and quantitative model

Here, we present a conceptual and quantitative model to describe the role of the Cytochrome [Formula: see text] complex in controlling steady-state electron transport in [Formula: see text] leaves. The model is based on new experimental methods to diagnose the maximum activity of Cyt [Formula: see text] in vivo, and to identify conditions under which photosynthetic control of Cyt [Formula: see text] is active or relaxed. With these approaches, we demonstrate that Cyt [Formula: see text] controls the trade-off between the speed and efficiency of electron transport under limiting light, and functions as a metabolic switch that transfers control to carbon metabolism under saturating light. We also present evidence that the onset of photosynthetic control of Cyt [Formula: see text] occurs within milliseconds of exposure to saturating light, much more quickly than the induction of non-photochemical quenching. We propose that photosynthetic control is the primary means of photoprotection and functions to manage excitation pressure, whereas non-photochemical quenching functions to manage excitation balance. We use these findings to extend the Farquhar et al. (Planta 149:78-90, 1980) model of [Formula: see text] photosynthesis to include a mechanistic description of the electron transport system. This framework relates the light captured by PS I and PS II to the energy and mass fluxes linking the photoacts with Cyt [Formula: see text], the ATP synthase, and Rubisco. It enables quantitative interpretation of pulse-amplitude modulated fluorometry and gas-exchange measurements, providing a new basis for analyzing how the electron transport system coordinates the supply of Fd, NADPH, and ATP with the dynamic demands of carbon metabolism, how efficient use of light is achieved under limiting light, and how photoprotection is achieved under saturating light. The model is designed to support forward as well as inverse applications. It can either be used in a stand-alone mode at the leaf-level or coupled to other models that resolve finer-scale or coarser-scale phenomena.

A rigorous evaluation of optimal peptide targets for MS-based clinical diagnostics of Coronavirus Disease 2019 (COVID-19)

BACKGROUND

The Coronavirus Disease 2019 (COVID-19) global pandemic has had a profound, lasting impact on the world's population. A key aspect to providing care for those with COVID-19 and checking its further spread is early and accurate diagnosis of infection, which has been generally done via methods for amplifying and detecting viral RNA molecules. Detection and quantitation of peptides using targeted mass spectrometry-based strategies has been proposed as an alternative diagnostic tool due to direct detection of molecular indicators from non-invasively collected samples as well as the potential for high-throughput analysis in a clinical setting; many studies have revealed the presence of viral peptides within easily accessed patient samples. However, evidence suggests that some viral peptides could serve as better indicators of COVID-19 infection status than others, due to potential misidentification of peptides derived from human host proteins, poor spectral quality, high limits of detection etc. METHODS: In this study we have compiled a list of 636 peptides identified from Sudden Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) samples, including from in vitro and clinical sources. These datasets were rigorously analyzed using automated, Galaxy-based workflows containing tools such as PepQuery, BLAST-P, and the Multi-omic Visualization Platform as well as the open-source tools MetaTryp and Proteomics Data Viewer (PDV).

RESULTS

Using PepQuery for confirming peptide spectrum matches, we were able to narrow down the 639-peptide possibilities to 87 peptides that were most robustly detected and specific to the SARS-CoV-2 virus. The specificity of these sequences to coronavirus taxa was confirmed using Unipept and BLAST-P. Through stringent p-value cutoff combined with manual verification of peptide spectrum match quality, 4 peptides derived from the nucleocapsid phosphoprotein and membrane protein were found to be most robustly detected across all cell culture and clinical samples, including those collected non-invasively.

CONCLUSION

We propose that these peptides would be of the most value for clinical proteomics applications seeking to detect COVID-19 from patient samples. We also contend that samples harvested from the upper respiratory tract and oral cavity have the highest potential for diagnosis of SARS-CoV-2 infection from easily collected patient samples using mass spectrometry-based proteomics assays.

Sizing of airborne particles in an operating room

Medical procedures that produce aerosolized particles are under great scrutiny due to the recent concerns surrounding the COVID-19 virus and increased risk for nosocomial infections. For example, thoracostomies, tracheotomies and intubations/extubations produce aerosols that can linger in the air. The lingering time is dependent on particle size where, e.g., 500 μm (0.5 mm) particles may quickly fall to the floor, while 1 μm particles may float for extended lengths of time. Here, a method is presented to characterize the size of <40 μm to >600 μm particles resulting from surgery in an operating room (OR). The particles are measured in-situ (next to a patient on an operating table) through a 75mm aperture in a ∼400 mm rectangular enclosure with minimal flow restriction. The particles and gasses exiting a patient are vented through an enclosed laser sheet while a camera captures images of the side-scattered light from the entrained particles. A similar optical configuration was described by Anfinrud et al.; however, we present here an extended method which provides a calibration method for determining particle size. The use of a laser sheet with side-scattered light provides a large FOV and bright image of the particles; however, the particle image dilation caused by scattering does not allow direct measurement of particle size. The calibration routine presented here is accomplished by measuring fixed particle distribution ranges with a calibrated shadow imaging system and mapping these measurements to the in-situ imaging system. The technique used for generating and measuring these particles is described. The result is a three-part process where 1) particles of varying sizes are produced and measured using a calibrated, high-resolution shadow imaging method, 2) the same particle generators are measured with the in-situ imaging system, and 3) a correlation mapping is made between the (dilated) laser image size and the measured particle size. Additionally, experimental and operational details of the imaging system are described such as requirements for the enclosure volume, light management, air filtration and control of various laser reflections. Details related to the OR environment and requirements for achieving close proximity to a patient are discussed as well.

Updates on metaQuantome Software for Quantitative Metaproteomics

metaQuantome is a software suite that enables the quantitative analysis, statistical evaluation. and visualization of mass-spectrometry-based metaproteomics data. In the latest update of this software, we have provided several extensions, including a step-by-step training guide, the ability to perform statistical analysis on samples from multiple conditions, and a comparative analysis of metatranscriptomics data. The training module, accessed via the Galaxy Training Network, will help users to use the suite effectively both for functional as well as for taxonomic analysis. We extend the ability of metaQuantome to now perform multi-data-point quantitative and statistical analyses so that studies with measurements across multiple conditions, such as time-course studies, can be analyzed. With an eye on the multiomics analysis of microbial communities, we have also initiated the use of metaQuantome statistical and visualization tools on outputs from metatranscriptomics data, which complements the metagenomic and metaproteomic analyses already available. For this, we have developed a tool named MT2MQ ("metatranscriptomics to metaQuantome"), which takes in outputs from the ASaiM metatranscriptomics workflow and transforms them so that the data can be used as an input for comparative statistical analysis and visualization via metaQuantome. We believe that these improvements to metaQuantome will facilitate the use of the software for quantitative metaproteomics and metatranscriptomics and will enable multipoint data analysis. These improvements will take us a step toward integrative multiomic microbiome analysis so as to understand dynamic taxonomic and functional responses of these complex systems in a variety of biological contexts. The updated metaQuantome and MT2MQ are open-source software and are available via the Galaxy Toolshed and GitHub.

A rigorous evaluation of optimal peptide targets for MS-based clinical diagnostics of Coronavirus Disease 2019 (COVID-19)

The Coronavirus Disease 2019 (COVID-19) global pandemic has had a profound, lasting impact on the world's population. A key aspect to providing care for those with COVID-19 and checking its further spread is early and accurate diagnosis of infection, which has been generally done via methods for amplifying and detecting viral RNA molecules. Detection and quantitation of peptides using targeted mass spectrometry-based strategies has been proposed as an alternative diagnostic tool due to direct detection of molecular indicators from non-invasively collected samples as well as the potential for high-throughput analysis in a clinical setting; many studies have revealed the presence of viral peptides within easily accessed patient samples. However, evidence suggests that some viral peptides could serve as better indicators of COVID-19 infection status than others, due to potential misidentification of peptides derived from human host proteins, poor spectral quality, high limits of detection etc. In this study we have compiled a list of 639 peptides identified from Sudden Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) samples, including from in vitro and clinical sources. These datasets were rigorously analyzed using automated, Galaxy-based workflows containing tools such as PepQuery, BLAST-P, and the Multi-omic Visualization Platform as well as the open-source tools MetaTryp and Proteomics Data Viewer (PDV). Using PepQuery for confirming peptide spectrum matches, we were able to narrow down the 639 peptide possibilities to 87 peptides which were most robustly detected and specific to the SARS-CoV-2 virus. The specificity of these sequences to coronavirus taxa was confirmed using Unipept and BLAST-P. Applying stringent statistical scoring thresholds, combined with manual verification of peptide spectrum match quality, 4 peptides derived from the nucleocapsid phosphoprotein and membrane protein were found to be most robustly detected across all cell culture and clinical samples, including those collected non-invasively. We propose that these peptides would be of the most value for clinical proteomics applications seeking to detect COVID-19 from a variety of sample types. We also contend that samples taken from the upper respiratory tract and oral cavity have the highest potential for diagnosis of SARS-CoV-2 infection from easily collected patient samples using mass spectrometry-based proteomics assays.

Inappropriate Implantable Cardioverter-Defibrillator Therapy With the Use of an Underbody Electrosurgery Dispersive Electrode

Perioperative management of implantable cardioverter-defibrillators is an important part of anesthetic care. Society recommendations and expert consensus statements exist to aid clinicians, and they have identified the umbilicus as an important landmark in decision-making. Implantable cardioverter-defibrillator antitachycardia therapy may not need to be deactivated for infraumbilical surgery because electromagnetic interference is unlikely to occur. The authors present two cases in which inappropriate antitachycardia therapy occurred intraoperatively with use of an underbody dispersive electrode, even though both surgeries were infraumbilical. The authors also present two cadaver models to demonstrate how monopolar electrosurgery below the umbilicus is sensed using both traditional and underbody dispersive electrosurgical return electrodes.

ASaiM-MT: a validated and optimized ASaiM workflow for metatranscriptomics analysis within Galaxy framework

The Earth Microbiome Project (EMP) aided in understanding the role of microbial communities and the influence of collective genetic material (the 'microbiome') and microbial diversity patterns across the habitats of our planet. With the evolution of new sequencing technologies, researchers can now investigate the microbiome and map its influence on the environment and human health. Advances in bioinformatics methods for next-generation sequencing (NGS) data analysis have helped researchers to gain an in-depth knowledge about the taxonomic and genetic composition of microbial communities. Metagenomic-based methods have been the most commonly used approaches for microbiome analysis; however, it primarily extracts information about taxonomic composition and genetic potential of the microbiome under study, lacking quantification of the gene products (RNA and proteins). On the other hand, metatranscriptomics, the study of a microbial community's RNA expression, can reveal the dynamic gene expression of individual microbial populations and the community as a whole, ultimately providing information about the active pathways in the microbiome.  In order to address the analysis of NGS data, the ASaiM analysis framework was previously developed and made available via the Galaxy platform. Although developed for both metagenomics and metatranscriptomics, the original publication demonstrated the use of ASaiM only for metagenomics, while thorough testing for metatranscriptomics data was lacking.  In the current study, we have focused on validating and optimizing the tools within ASaiM for metatranscriptomics data. As a result, we deliver a robust workflow that will enable researchers to understand dynamic functional response of the microbiome in a wide variety of metatranscriptomics studies. This improved and optimized ASaiM-metatranscriptomics (ASaiM-MT) workflow is publicly available via the ASaiM framework, documented and supported with training material so that users can interrogate and characterize metatranscriptomic data, as part of larger meta-omic studies of microbiomes.

ASaiM-MT: a validated and optimized ASaiM workflow for metatranscriptomics analysis within Galaxy framework

The Human Microbiome Project (HMP) aided in understanding the role of microbial communities and the influence of collective genetic material (the 'microbiome') in human health and disease. With the evolution of new sequencing technologies, researchers can now investigate the microbiome and map its influence on human health. Advances in bioinformatics methods for next-generation sequencing (NGS) data analysis have helped researchers to gain an in-depth knowledge about the taxonomic and genetic composition of microbial communities. Metagenomic-based methods have been the most commonly used approaches for microbiome analysis; however, it primarily extracts information about taxonomic composition and genetic potential of the microbiome under study, lacking quantification of the gene products (RNA and proteins). Conversely, metatranscriptomics, the study of a microbial community's RNA expression, can reveal the dynamic gene expression of individual microbial populations and the community as a whole, ultimately providing information about the active pathways in the microbiome.  In order to address the analysis of NGS data, the ASaiM analysis framework was previously developed and made available via the Galaxy platform. Although developed for both metagenomics and metatranscriptomics, the original publication demonstrated the use of ASaiM only for metagenomics, while thorough testing for metatranscriptomics data was lacking.  In the current study, we have focused on validating and optimizing the tools within ASaiM for metatranscriptomics data. As a result, we deliver a robust workflow that will enable researchers to understand dynamic functional response of the microbiome in a wide variety of metatranscriptomics studies. This improved and optimized ASaiM-metatranscriptomics (ASaiM-MT) workflow is publicly available via the ASaiM framework, documented and supported with training material so that users can interrogate and characterize metatranscriptomic data, as part of larger meta-omic studies of microbiomes.

Results From an Evidenced-Based Curriculum Design With Innovative Simulators to Prepare Providers in Caring for Those With Burn Injuries

Burn care and medical education have undergone dramatic changes. Trauma has over seven courses covering fundamentals, whereas burns has one. Our goal was to develop a course to meet the needs of healthcare professionals requiring more advanced burn management training. A survey was distributed to burn physicians, nurses, therapists, administrators, and survivors, to assess the perceived proficiency of those managing adult and pediatric patients. Procedure simulators were developed, and a course was designed and delivered. An after-course survey of participants captured how this course filled identified knowledge gaps. A total of 188 initial surveys were sent to individuals involved in burn care. A diverse pool of 109 individuals participated (58% response rate). Survey results by providers demonstrated the lowest self-rated proficiency scores at managing large pediatric burns and frostbite. Nonphysicians reported low proficiency in developing wound treatment algorithms, performing escharotomies, and aftercare/reintegration. Following rigorous curriculum development, the course was conducted, and after-course surveys noted students' improved understanding of managing burn injuries, ability to troubleshoot, confidence to manage patients, and their recommending the course to a peer. Providing quality care beyond the initial assessment and stabilization of a burn-injured patient requires additional skills and knowledge. Providers that are uncomfortable or challenged in providing this care may benefit from additional training. Initial data show that a course, such as this one, provides the education necessary to fill the most commonly reported gaps in knowledge and skills. Further work is being invested to develop disaster management skills, assessment components, and further determine course validity.

Crustal processes sustain Arctic abiotic gas hydrate and fluid flow systems

The Svyatogor Ridge and surroundings, located on the sediment-covered western flank of the Northern Knipovich Ridge, host extensive gas hydrate and related fluid flow systems. The fluid flow system here manifests in the upper sedimentary sequence as gas hydrates and free gas, indicated by bottom simulating reflections (BSRs) and amplitude anomalies. Using 2D seismic lines and bathymetric data, we map tectonic features such as faults, crustal highs, and indicators of fluid flow processes. Results indicate a strong correlation between crustal faults, crustal highs and fluid accumulations in the overlying sediments, as well as an increase in geothermal gradient over crustal faults. We conclude here that gas generated during the serpentinization of exhumed mantle rocks drive the extensive occurrence of gas hydrate and fluid flow systems in the region and transform faults act as an additional major pathway for fluid circulation.

Multi-omics Visualization Platform: An extensible Galaxy plug-in for multi-omics data visualization and exploration

BACKGROUND

Proteogenomics integrates genomics, transcriptomics, and mass spectrometry (MS)-based proteomics data to identify novel protein sequences arising from gene and transcript sequence variants. Proteogenomic data analysis requires integration of disparate 'omic software tools, as well as customized tools to view and interpret results. The flexible Galaxy platform has proven valuable for proteogenomic data analysis. Here, we describe a novel Multi-omics Visualization Platform (MVP) for organizing, visualizing, and exploring proteogenomic results, adding a critically needed tool for data exploration and interpretation.

FINDINGS

MVP is built as an HTML Galaxy plug-in, primarily based on JavaScript. Via the Galaxy API, MVP uses SQLite databases as input-a custom data type (mzSQLite) containing MS-based peptide identification information, a variant annotation table, and a coding sequence table. Users can interactively filter identified peptides based on sequence and data quality metrics, view annotated peptide MS data, and visualize protein-level information, along with genomic coordinates. Peptides that pass the user-defined thresholds can be sent back to Galaxy via the API for further analysis; processed data and visualizations can also be saved and shared. MVP leverages the Integrated Genomics Viewer JavaScript framework, enabling interactive visualization of peptides and corresponding transcript and genomic coding information within the MVP interface.

CONCLUSIONS

MVP provides a powerful, extensible platform for automated, interactive visualization of proteogenomic results within the Galaxy environment, adding a unique and critically needed tool for empowering exploration and interpretation of results. The platform is extensible, providing a basis for further development of new functionalities for proteogenomic data visualization.

FluChip-8G Insight: HA and NA subtyping of potentially pandemic influenza A viruses in a single assay

BACKGROUND

Global influenza surveillance in humans and animals is a critical component of pandemic preparedness. The FluChip-8G Insight assay was developed to subtype both seasonal and potentially pandemic influenza viruses in a single assay with a same day result. FluChip-8G Insight uses whole gene segment RT-PCR-based amplification to provide robustness against genetic drift and subsequent microarray detection with artificial neural network-based data interpretation.

OBJECTIVES

The objective of this study was to verify and validate the performance of the FluChip-8G Insight assay for the detection and positive identification of human and animal origin non-seasonal influenza A specimens.

METHODS

We evaluated the ability of the FluChip-8G Insight technology to type and HA and NA subtype a sample set consisting of 297 results from 180 unique non-seasonal influenza A strains (49 unique subtypes).

RESULTS

FluChip-8G Insight demonstrated a positive percent agreement ≥93% for 5 targeted HA and 5 targeted NA subtypes except for H9 (88%), and negative percent agreement exceeding 95% for all targeted subtypes.

CONCLUSIONS

The FluChip-8G Insight neural network-based algorithm used for virus identification performed well over a data set of 297 naïve sample results, and can be easily updated to improve performance on emerging strains without changing the underlying assay chemistry.

Clinical validation of the FluChip-8G Influenza A+B Assay for influenza type and subtype identification

BACKGROUND

The FluChip-8G Influenza A+B Assay is a multiplexed influenza RT-PCR and microarray-based assay with same day turnaround time, developed to subtype seasonal A viruses (H1N1pdm2009 and H3N2), distinguish B viruses as Yamagata or Victoria lineage, and is the only FDA cleared assay capable of positive identification of a wide variety of A subtypes as "non-seasonal" A viruses from human nasal specimens.

OBJECTIVE

To evaluate clinical performance of the FluChip-8G Influenza A+B Assay for detection of seasonal influenza viruses in nasal and nasopharyngeal swab specimens, and to evaluate performance for detection of non-seasonal influenza viruses using contrived samples.

STUDY DESIGN

For seasonal viruses, a multisite study of the FluChip-8G Influenza A+B Assay using prospectively and retrospectively collected nasal and nasopharyngeal swabs was performed using the FDA-cleared CDC Human Flu Dx Panel as the comparator assay. For non-seasonal viruses, testing was performed at a single site using contrived samples from 100 unique non-seasonal strains representing 41 subtypes.

RESULTS

Sensitivity (95% CI) and specificity (95% CI) for each target group, respectively, from results of 1689 clinical specimens were: seasonal H1N1pdm2009: 96.4% (87.9-99.0), 99.3% (98.8-99.6), seasonal H3N2: 91.8% (87.7-94.7), 99.7% (99.2-99.9), Influenza B Victoria: 100% (94.0-100.0), 99.9% (99.6-100.0), and Influenza B Yamagata: 95.6% (89.2-98.3), 99.9% (99.6-100.0). The sensitivity and specificity from contrived influenza A non-seasonal viruses was determined to be 99.0% (94.6-99.8) and 100% (96.7-100.0).

CONCLUSION

The FluChip-8G Influenza A+B Assay has robust sensitivity and specificity for detecting and identifying all target virus groups, including non-seasonal influenza A, with same day results.

The Golden Opportunity: Multidisciplinary Simulation Training Improves Trauma Team Efficiency

OBJECTIVE

Every trauma patient has a golden hour, and resuscitation efficiency within that hour has large implications for patients. We instituted simulation based trauma resuscitation training with the hypothesis that it would improve trauma team efficiency.

METHODS

Five simulation training sessions were conducted with immediate debriefing. Metrics collected in actual trauma resuscitations before and after simulation training included time of primary and secondary surveys and time to computed tomography (CT) scan. Study participants were from multidisciplinary specialties involved in trauma resuscitations as well as former trauma patients from the Trauma Survivors Network.

RESULTS

Seventy-three patients undergoing trauma resuscitations were screened and 67 patients were included. Time to CT scan and secondary survey completion were significantly reduced in actual trauma patient activations following implementation of the curriculum (reduction of 23 to 16 minutes for CT scan p < 0.05, and reduction from 14 to 6 minutes for secondary survey, p < 0.05). Time to primary survey completion did not change (5 minutes).

CONCLUSIONS

Multidisciplinary simulation training was associated with improved trauma team efficiency in the form of reduced assessment time. As emergency department length of stay is an independent predictor of hospital mortality following trauma activation, team-based simulation training has the potential to improve patient outcomes. Multidisciplinary involvement was a key factor, and Trauma Survivors Network involvement brought credibility from the patient perspective.

Fecal Coliform Bacterial Detection to Assess Enema Adherence in HIV Prevention Clinical Studies

Evaluating the efficacy of any HIV prevention strategy is dependent on ensuring and objectively monitoring adherence to the intervention. Medicated rectal enemas are a potential method for providing topical, episodic HIV prophylaxis during receptive anal intercourse. Assessing adherence to recommended enema dosing regimens is essential in evaluating the utility of this strategy. We utilized fecal coliform bacteria on used enema tips as a marker for enema use. Enema tip coliforms were tested by repurposing a microtiter plate-based water quality test designed to detect fecal contamination of water. Coliform detection occurred with 100% sensitivity and specificity when tips were assayed on day of use. The assay performed well post-7 day sample storage at room temperature, yielding a sensitivity of 80% and specificity of 93%. All (n = 64) samples collected in a subset of the DREAM-01 rectal microbicide enema clinical trial tested positive, even when tips were evaluated > 7 days post-reported use. The coliform-based enema tip assay allows monitoring of adherence in interventions involving rectal enemas in a sensitive, specific and inexpensive manner. The test performs well in clinical trial settings.

Improve your Galaxy text life: The Query Tabular Tool

Galaxy provides an accessible platform where multi-step data analysis workflows integrating disparate software can be run, even by researchers with limited programming expertise. Applications of such sophisticated workflows are many, including those which integrate software from different ‘omic domains (e.g. genomics, proteomics, metabolomics). In these complex workflows, intermediate outputs are often generated as tabular text files, which must be transformed into customized formats which are compatible with the next software tools in the pipeline. Consequently, many text manipulation steps are added to an already complex workflow, overly complicating the process. In some cases, limitations to existing text manipulation are such that desired analyses can only be carried out using highly sophisticated processing steps beyond the reach of even advanced users and developers. For users with some SQL knowledge, these text operations could be combined into single, concise query on a relational database. As a solution, we have developed the Query Tabular Galaxy tool, which leverages a SQLite database generated from tabular input data. This database can be queried and manipulated to produce transformed and customized tabular outputs compatible with downstream processing steps. Regular expressions can also be utilized for even more sophisticated manipulations, such as find and replace and other filtering actions. Using several Galaxy-based multi-omic workflows as an example, we demonstrate how the Query Tabular tool dramatically streamlines and simplifies the creation of multi-step analyses, efficiently enabling complicated textual manipulations and processing. This tool should find broad utility for users of the Galaxy platform seeking to develop and use sophisticated workflows involving text manipulation on tabular outputs.

Improve your Galaxy text life: The Query Tabular Tool

Galaxy provides an accessible platform where multi-step data analysis workflows integrating disparate software can be run, even by researchers with limited programming expertise. Applications of such sophisticated workflows are many, including those which integrate software from different 'omic domains (e.g. genomics, proteomics, metabolomics). In these complex workflows, intermediate outputs are often generated as tabular text files, which must be transformed into customized formats which are compatible with the next software tools in the pipeline. Consequently, many text manipulation steps are added to an already complex workflow, overly complicating the process. In some cases, limitations to existing text manipulation are such that desired analyses can only be carried out using highly sophisticated processing steps beyond the reach of even advanced users and developers. For users with some SQL knowledge, these text operations could be combined into single, concise query on a relational database. As a solution, we have developed the Query Tabular Galaxy tool, which leverages a SQLite database generated from tabular input data. This database can be queried and manipulated to produce transformed and customized tabular outputs compatible with downstream processing steps. Regular expressions can also be utilized for even more sophisticated manipulations, such as find and replace and other filtering actions. Using several Galaxy-based multi-omic workflows as an example, we demonstrate how the Query Tabular tool dramatically streamlines and simplifies the creation of multi-step analyses, efficiently enabling complicated textual manipulations and processing. This tool should find broad utility for users of the Galaxy platform seeking to develop and use sophisticated workflows involving text manipulation on tabular outputs.

Interdependent Tripartite Efficacy Perceptions and Individual Performance: Case Study of a Boys' Basketball Team

Tripartite efficacy refers to the beliefs of the individuals within a dyad regarding personal abilities (self-efficacy), the partner's abilities (other-efficacy), or relation-inferred self-efficacy. This efficacy model has recently gained popularity in sports research (Jackson, Whipp, & Beauchamp, 2013), although there has not been any longitudinal research on efficacy beliefs and performance within this complex intra-dyad tripartite efficacy model. In a case study, we examined six individual players on a high school basketball team to explore any longitudinal changes in these tripartite efficacy beliefs through a season of play. On seven data collection periods, players completed the Basketball-Tripartite Efficacy Measure, and their game performance statistics were analyzed with an objective basketball individual performance formula. We found similar variations between participants' other-efficacy beliefs and the dyad partner's basketball performance score as well as between self-efficacy and individual performance score. Observational data from this case study lend some support to spiraling of self-efficacy and performance from repeated successes or failures and to perceived efficacy-performance plateaus that have been previously demonstrated in controlled experimental research. Importantly, this study suggests the presence of other-efficacy beliefs in their relationship to other-performance and to spiraling relationships between other-efficacy beliefs and other-performance, which have not been demonstrated previously.

Gas hydrate dissociation off Svalbard induced by isostatic rebound rather than global warming

Methane seepage from the upper continental slopes of Western Svalbard has previously been attributed to gas hydrate dissociation induced by anthropogenic warming of ambient bottom waters. Here we show that sediment cores drilled off Prins Karls Foreland contain freshwater from dissociating hydrates. However, our modeling indicates that the observed pore water freshening began around 8 ka BP when the rate of isostatic uplift outpaced eustatic sea-level rise. The resultant local shallowing and lowering of hydrostatic pressure forced gas hydrate dissociation and dissolved chloride depletions consistent with our geochemical analysis. Hence, we propose that hydrate dissociation was triggered by postglacial isostatic rebound rather than anthropogenic warming. Furthermore, we show that methane fluxes from dissociating hydrates were considerably smaller than present methane seepage rates implying that gas hydrates were not a major source of methane to the oceans, but rather acted as a dynamic seal, regulating methane release from deep geological reservoirs.

Methane seepage from continental slopes has been attributed to gas hydrate dissociation induced by anthropogenic bottom water warming. Here, the authors show that hydrates dissociated before the Anthropocene when the isostatic rebound induced by deglaciation of the Arctic ice sheet outpaced eustatic sea-level rise.

An Accessible Proteogenomics Informatics Resource for Cancer Researchers

Proteogenomics has emerged as a valuable approach in cancer research, which integrates genomic and transcriptomic data with mass spectrometry-based proteomics data to directly identify expressed, variant protein sequences that may have functional roles in cancer. This approach is computationally intensive, requiring integration of disparate software tools into sophisticated workflows, challenging its adoption by nonexpert, bench scientists. To address this need, we have developed an extensible, Galaxy-based resource aimed at providing more researchers access to, and training in, proteogenomic informatics. Our resource brings together software from several leading research groups to address two foundational aspects of proteogenomics: (i) generation of customized, annotated protein sequence databases from RNA-Seq data; and (ii) accurate matching of tandem mass spectrometry data to putative variants, followed by filtering to confirm their novelty. Directions for accessing software tools and workflows, along with instructional documentation, can be found at z.umn.edu/canresgithub. Cancer Res; 77(21); e43-46. ©2017 AACR.

Association of a Surgical Task During Training With Team Skill Acquisition Among Surgical Residents: The Missing Piece in Multidisciplinary Team Training

Importance

The human patient simulators that are currently used in multidisciplinary operating room team training scenarios cannot simulate surgical tasks because they lack a realistic surgical anatomy. Thus, they eliminate the surgeon's primary task in the operating room. The surgical trainee is presented with a significant barrier when he or she attempts to suspend disbelief and engage in the scenario.

Objective

To develop and test a simulation-based operating room team training strategy that challenges the communication abilities and teamwork competencies of surgeons while they are engaged in realistic operative maneuvers.

Design, Setting, and Participants

This pre-post educational intervention pilot study compared the gains in teamwork skills for midlevel surgical residents at Wake Forest Baptist Medical Center after they participated in a standardized multidisciplinary team training scenario with 3 possible levels of surgical realism: (1) SimMan (Laerdal) (control group, no surgical anatomy); (2) "synthetic anatomy for surgical tasks" mannequin (medium-fidelity anatomy), and (3) a patient simulated by a deceased donor (high-fidelity anatomy).

Interventions

Participation in the simulation scenario and the subsequent debriefing.

Main Outcomes and Measures

Teamwork competency was assessed using several instruments with extensive validity evidence, including the Nontechnical Skills assessment, the Trauma Management Skills scoring system, the Crisis Resource Management checklist, and a self-efficacy survey instrument. Participant satisfaction was assessed with a Likert-scale questionnaire.

Results

Scenario participants included midlevel surgical residents, anesthesia providers, scrub nurses, and circulating nurses. Statistical models showed that surgical residents exposed to medium-fidelity simulation (synthetic anatomy for surgical tasks) team training scenarios demonstrated greater gains in teamwork skills compared with control groups (SimMan) (Nontechnical Skills video score: 95% CI, 1.06-16.41; Trauma Management Skills video score: 95% CI, 0.61-2.90) and equivalent gains in teamwork skills compared with high-fidelity simulations (deceased donor) (Nontechnical Skills video score: 95% CI, -8.51 to 6.71; Trauma Management Skills video score: 95% CI, -1.70 to 0.49).

Conclusions and Relevance

Including a surgical task in operating room team training significantly enhanced the acquisition of teamwork skills among midlevel surgical residents. Incorporating relatively inexpensive, medium-fidelity synthetic anatomy in human patient simulators was as effective as using high-fidelity anatomies from deceased donors for promoting teamwork skills in this learning group.

Cobalamin riboswitches exhibit a broad range of ability to discriminate between methylcobalamin and adenosylcobalamin

Riboswitches are a widely distributed class of regulatory RNAs in bacteria that modulate gene expression via small-molecule-induced conformational changes. Generally, these RNA elements are grouped into classes based upon conserved primary and secondary structure and their cognate effector molecule. Although this approach has been very successful in identifying new riboswitch families and defining their distributions, small sequence differences between structurally related RNAs can alter their ligand selectivity and regulatory behavior. Herein, we use a structure-based mutagenic approach to demonstrate that cobalamin riboswitches have a broad spectrum of preference for the two biological forms of cobalamin in vitro using isothermal titration calorimetry. This selectivity is primarily mediated by the interaction between a peripheral element of the RNA that forms a T-loop module and a subset of nucleotides in the cobalamin-binding pocket. Cell-based fluorescence reporter assays in Escherichia coli revealed that mutations that switch effector preference in vitro lead to differential regulatory responses in a biological context. These data demonstrate that a more comprehensive analysis of representative sequences of both previously and newly discovered classes of riboswitches might reveal subgroups of RNAs that respond to different effectors. Furthermore, this study demonstrates a second distinct means by which tertiary structural interactions in cobalamin riboswitches dictate ligand selectivity.

Performance of induction module cavity ring-down spectroscopy (IM-CRDS) for measuring δ18 O and δ2 H values of soil, stem, and leaf waters

RATIONALE

Induction module cavity ring-down spectroscopy (IM-CRDS) has been proposed as a rapid and cost-effective alternative to cryogenic vacuum distillation (CVD) and isotope ratio mass spectrometry (IRMS) for the measurement of δ¹⁸ O and δ² H values in matrix-bound waters. In the current study, we characterized the performance of IM-CRDS relative to CVD and IRMS and investigated the mechanisms responsible for differences between the methods.

METHODS

We collected a set of 75 soil, stem, and leaf water samples, and measured the δ¹⁸ O and δ² H values of each sample with four techniques: CVD and IRMS, CVD and CRDS, CVD and IM-CRDS, and IM-CRDS alone. We then calculated the isotopic errors for each of the three CRDS methods relative to CVD and IRMS, and analyzed the relationships among these errors and suites of diagnostic spectral parameters that are indicative of organic contamination.

RESULTS

The IM-CRDS technique accurately assessed the δ¹⁸ O and δ² H values of pure waters, but exhibited progressively increasing errors for soil waters, stem waters, and leaf waters. For soils, the errors were attributable to subsampling of isotopically heterogeneous source material, whereas for stems and leaves, they were attributable to spectral interference. Unexpectedly, the magnitude of spectral interference was higher for the solid samples analyzed directly via IM-CRDS than for those originally extracted via CVD and then analyzed by IM-CRDS.

CONCLUSIONS

There are many types of matrix-bound water samples for which IM-CRDS measurements include significant errors from spectral interference. As a result, spectral analysis and validation should be incorporated into IM-CRDS post-processing procedures. In the future, IM-CRDS performance could be improved through: (i) identification of the compounds that cause spectral interference, and either (ii) modification of the combustion step to completely oxidize these compounds to CO₂ , and/or (iii) incorporation of corrections for these compounds into the spectral fitting models used by the CRDS analyzers. Copyright © 2016 John Wiley & Sons, Ltd.

Complex interactive effects of water mold, herbicide, and the fungus Batrachochytrium dendrobatidis on Pacific treefrog Hyliola regilla hosts

Infectious diseases pose a serious threat to global biodiversity. However, their ecological impacts are not independent of environmental conditions. For example, the pathogenic fungus Batrachochytrium dendrobatidis (Bd), which has contributed to population declines and extinctions in many amphibian species, interacts with several environmental factors to influence its hosts, but potential interactions with other pathogens and environmental contaminants are understudied. We examined the combined effects of Bd, a water mold (Achlya sp.), and the herbicide Roundup® Regular (hereafter, Roundup®) on larval Pacific treefrog Hyliola regilla hosts. We employed a 2 wk, fully factorial laboratory experiment with 3 ecologically realistic levels (0, 1, and 2 mg l-1 of active ingredient) of field-formulated Roundup®, 2 Achlya treatments (present and absent), and 2 Bd treatments (present and absent). Our results were consistent with sublethal interactive effects involving all 3 experimental factors. When Roundup® was absent, the proportion of Bd-exposed larvae infected with Bd was elevated in the presence of Achlya, consistent with Achlya acting as a synergistic cofactor that facilitated the establishment of Bd infection. However, this Achlya effect became nonsignificant at 1 mg l-1 of the active ingredient of Roundup® and disappeared at the highest Roundup® concentration. In addition, Roundup® decreased Bd loads among Bd-exposed larvae. Our study suggests complex interactive effects of a water mold and a contaminant on Bd infection in amphibian hosts. Achlya and Roundup® were both correlated with altered patterns of Bd infection, but in different ways, and Roundup® appeared to remove the influence of Achlya on Bd.

Using Galaxy for Proteomics

The area of informatics for mass spectrometry (MS)-based proteomics data has steadily grown over the last two decades. Numerous, effective software programs now exist for various aspects of proteomic informatics. However, many researchers still have difficulties in using these software. These difficulties arise from problems with running and integrating disparate software programs, scalability issues when dealing with large data volumes, and lack of ability to share and reproduce workflows comprised of different software. The Galaxy framework for bioinformatics provides an attractive option for solving many of these current issues in proteomic informatics. Originally developed as a workbench to enable genomic data analysis, numerous researchers are now turning to Galaxy to implement software for MS-based proteomics applications. Here, we provide an introduction to Galaxy and its features, and describe how software tools are deployed, published and shared via the scalable framework. We also describe some of the existing tools in Galaxy for basic MS-based proteomics data analysis and informatics. Finally, we describe how proteomics tools in Galaxy can be combined with other existing tools for genomic and transcriptomic data analysis to enable powerful multi-omic data analysis applications.

Hybrid Valve Structure for High-Throughput, Low-Volume Liquid-Handling Applications

A hybrid valve that integrates precision microfluidics for fluid routing, high-speed valving for fluid switching, and reagent-jetting devices for metering the fluid dispenses is described. The hybrid valve enables parallel switching between aspiration and dispense modes for multiple sample streams. This unique valve structure addresses many of the concerns with handling microscale volumes, including efficient use of samples, degradation of ink jet valves and speed of operation. A broad range of volumes can be manipulated with excellent reproducibility. The hybrid valve can be configured for a variety of applications. Pick-and-place aspiration and dispensing of unique reagents and rapid dispensing of a common reagent are possible. Together these features lead to higher-speed transfer of smaller volumes of reagent.

Protein relative abundance patterns associated with sucrose-induced dysbiosis are conserved across taxonomically diverse oral microcosm biofilm models of dental caries

BACKGROUND

The etiology of dental caries is multifactorial, but frequent consumption of free sugars, notably sucrose, appears to be a major factor driving the supragingival microbiota in the direction of dysbiosis. Recent 16S rRNA-based studies indicated that caries-associated communities were less diverse than healthy supragingival plaque but still displayed considerable taxonomic diversity between individuals. Metagenomic studies likewise have found that healthy oral sites from different people were broadly similar with respect to gene function, even though there was an extensive individual variation in their taxonomic profiles. That pattern may also extend to dysbiotic communities. In that case, shifts in community-wide protein relative abundance might provide better biomarkers of dysbiosis that can be achieved through taxonomy alone.

RESULTS

In this study, we used a paired oral microcosm biofilm model of dental caries to investigate differences in community composition and protein relative abundance in the presence and absence of sucrose. This approach provided large quantities of protein, which facilitated deep metaproteomic analysis. Community composition was evaluated using 16S rRNA sequencing and metaproteomic approaches. Although taxonomic diversity was reduced by sucrose pulsing, considerable inter-subject variation in community composition remained. By contrast, functional analysis using the SEED ontology found that sucrose induced changes in protein relative abundance patterns for pathways involving glycolysis, lactate production, aciduricity, and ammonia/glutamate metabolism that were conserved across taxonomically diverse dysbiotic oral microcosm biofilm communities.

CONCLUSIONS

Our findings support the concept of using function-based changes in protein relative abundance as indicators of dysbiosis. Our microcosm model cannot replicate all aspects of the oral environment, but the deep level of metaproteomic analysis it allows makes it suitable for discovering which proteins are most consistently abundant during dysbiosis. It then may be possible to define biomarkers that could be used to detect at-risk tooth surfaces before the development of overt carious lesions.

NCBI BLAST+ integrated into Galaxy

Background

The NCBI BLAST suite has become ubiquitous in modern molecular biology and is used for small tasks such as checking capillary sequencing results of single PCR products, genome annotation or even larger scale pan-genome analyses. For early adopters of the Galaxy web-based biomedical data analysis platform, integrating BLAST into Galaxy was a natural step for sequence comparison workflows.

Findings

The command line NCBI BLAST+ tool suite was wrapped for use within Galaxy. Appropriate datatypes were defined as needed. The integration of the BLAST+ tool suite into Galaxy has the goal of making common BLAST tasks easy and advanced tasks possible.

Conclusions

This project is an informal international collaborative effort, and is deployed and used on Galaxy servers worldwide. Several examples of applications are described here.

Flexible and accessible workflows for improved proteogenomic analysis using the Galaxy framework

Proteogenomics combines large-scale genomic and transcriptomic data with mass-spectrometry-based proteomic data to discover novel protein sequence variants and improve genome annotation. In contrast with conventional proteomic applications, proteogenomic analysis requires a number of additional data processing steps. Ideally, these required steps would be integrated and automated via a single software platform offering accessibility for wet-bench researchers as well as flexibility for user-specific customization and integration of new software tools as they emerge. Toward this end, we have extended the Galaxy bioinformatics framework to facilitate proteogenomic analysis. Using analysis of whole human saliva as an example, we demonstrate Galaxy’s flexibility through the creation of a modular workflow incorporating both established and customized software tools that improve depth and quality of proteogenomic results. Our customized Galaxy-based software includes automated, batch-mode BLASTP searching and a Peptide Sequence Match Evaluator tool, both useful for evaluating the veracity of putative novel peptide identifications. Our complex workflow (approximately 140 steps) can be easily shared using built-in Galaxy functions, enabling their use and customization by others. Our results provide a blueprint for the establishment of the Galaxy framework as an ideal solution for the emerging field of proteogenomics.

Using Galaxy-P to leverage RNA-Seq for the discovery of novel protein variations

BACKGROUND

Current practice in mass spectrometry (MS)-based proteomics is to identify peptides by comparison of experimental mass spectra with theoretical mass spectra derived from a reference protein database; however, this strategy necessarily fails to detect peptide and protein sequences that are absent from the database. We and others have recently shown that customized proteomic databases derived from RNA-Seq data can be employed for MS-searching to both improve MS analysis and identify novel peptides. While this general strategy constitutes a significant advance for the discovery of novel protein variations, it has not been readily transferable to other laboratories due to the need for many specialized software tools. To address this problem, we have implemented readily accessible, modifiable, and extensible workflows within Galaxy-P, short for Galaxy for Proteomics, a web-based bioinformatic extension of the Galaxy framework for the analysis of multi-omics (e.g. genomics, transcriptomics, proteomics) data.

RESULTS

We present three bioinformatic workflows that allow the user to upload raw RNA sequencing reads and convert the data into high-quality customized proteomic databases suitable for MS searching. We show the utility of these workflows on human and mouse samples, identifying 544 peptides containing single amino acid polymorphisms (SAPs) and 187 peptides corresponding to unannotated splice junction peptides, correlating protein and transcript expression levels, and providing the option to incorporate transcript abundance measures within the MS database search process (reduced databases, incorporation of transcript abundance for protein identification score calculations, etc.).

CONCLUSIONS

Using RNA-Seq data to enhance MS analysis is a promising strategy to discover novel peptides specific to a sample and, more generally, to improve proteomics results. The main bottleneck for widespread adoption of this strategy has been the lack of easily used and modifiable computational tools. We provide a solution to this problem by introducing a set of workflows within the Galaxy-P framework that converts raw RNA-Seq data into customized proteomic databases.