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At least 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Plant Microbiomes May Provide Vital Information to Plant Success

Plant associated microbiomes, the rhizosphere and phyllosphere, are composed of communities of bacteria and fungi that may be mutualistic or pathogenic. These communities have the potential to influence plant health and development and can affect plant growth. Crop plants are being investigated as a fresh and safe supplement to astronauts’ diet and it is critical to understand and characterize these microbial communities. Multi-species crops, Mizuna mustard (Brassica rapa var japonica), ‘Outredgeous’ red romaine lettuce (Lactuca sativa), and Waldman’s Green lettuce (Lactuca sativa) were grown in two Veggie units on the International Space Station (ISS) for three grow outs in various combinations of plant types. Upon harvest, plant and pillow samples were frozen and returned to Earth for analysis. Bacterial and fungal community analyses for plant leaf and root, as well as pillow components, wick and media, were completed using next generation sequencing with the goal of surveying the composition of the entire community and identifying any potential pathogens. Bacteria were identified using the 16S rRNA gene whereas, fungi were identified with the internal transcribed spacer (ITS). The community composition for these three crops was compared between crop types and between plant tissue types. It is vital to mission success for the short term and long term to add nutritious, safe to eat vegetables providing a supplement to the crew members’ dietary requirements as well as to develop planning for deep space missions as we reach for the moon and on to Mars. Veggie technology validation tests were supported by NASA’s Space Biology Program.

Khodadad, Christina L.↗
NASA NTRSDate not supplied

Exploring Sustained Food Production in the Veggie Vegetable Production System

Since 2014, the Veggie vegetable production system on the International Space Station has intermittently supplied astronaut crews with fresh produce. To assess the potential for continuous crop production in Veggie and develop a baseline for future space crop production systems, a 120-day study was conducted to determine methodology for inputs, optimal yield, food safety standards, and crew involvement. ‘Amara’ mustard and ‘Red Russian’ kale were grown as initial crops, followed by ‘Extra Dwarf’ pakchoi and shungiku as final crops. Previous grow-outs in Veggie have included harvests at 28-35 days after initiation. In this study, a 56-day grow-out with multiple harvests from the same plants was compared to the conventional, single harvest Veggie schedule. Unlike previous Veggie studies which grew all plants simultaneously, this test staggered initiation and harvest, aiming for consistent and increased production. Plant pillows were initiated in pairs weekly and positioned to reduce shading. Completed pillows were immediately replaced with fresh ones. The multi-harvest scheme used fewer pillows, totaling 46% less pillow mass than the single harvest method. Yield varied by crop and harvest scheme, when compared as fresh edible biomass production across 56-day time increments. ‘Red Russian’ kale yielded similarly across harvest schemes. In the multiple-harvest schedule, ‘Amara’ mustard and shungiku yielded 23% to 25% higher, respectively, while ‘Extra Dwarf’ pakchoi had 43% lower yield. Microbial analysis of the plants indicated no culturable human pathogens. Microbial load of a given plant appeared to depend more on system age than plant age; across harvest methods, aerobic plate counts from final crops were higher than those of initial crops. This project also considered the complexity of crew involvement in a continuous production scenario. New crew procedures that periodically remove plant material from the Veggie root mat would be needed under continuous production to prevent potential pathogens and unpleasant odors. This study supports future space crop production scenarios and was funded by NASA’s Human Research Program.

Jess M Bunchek↗
NASA NTRSDate not supplied

Spaceflight-Induced Changes in Microbial Virulence and the Impact to the Host Immune Response

Over the past 50 years, microorganisms have displayed unexpected responses relevant to infectious disease when grown in spaceflight and spaceflight analogues, including changes in final cell concentration, biofilm production, stress resistance, antibiotic sensitivity, gene expression, and virulence. •Seminal studies demonstrated that the foodborne pathogen, Salmonella enterica serovar Typhimurium, increased its virulence and pathogenesis-related characteristics in response to both spaceflight and spaceflight analogue culture. Since those experiments, alterations in the pathogenesis-related characteristics of other pathogens have been documented in response to growth in these environments4,5,6, and a recent study has demonstrated an increase in virulence of Serratia marcescens cultured during spaceflight7. However, our overall knowledge of which microorganisms will alter their virulence in response to spaceflight and spaceflight analogue culture remains very limited

C. M. Ott↗
NASA NTRS2017

Microbial Characteristics of ISS Environmental Surfaces

The microbiome of environmental surfaces from the International Space Station were characterized in order to examine the relationship to crew and hardware maintenance. The Microbial Observatory (ISS-MO) experiment generated a microbial census of ISS environments using advanced molecular microbial community analyses along with traditional culture-based methods. Since the “omics” methodologies generated an extensive microbial census, significant insights into spaceflight-induced changes in the populations of beneficial and/or potentially harmful microbes were gained. Surface samples were collected from several ISS surface locations from three flight opportunities, and were returned to Earth via the Soyuz TMA-14M or the Space X Dragon capsule. In addition to cultivation methods, viable microbial burden, iTag-based sequencing, and metagenome analyses were carried out. The cultivable microbial bioburden differed by location and sampling event. Exploring the ISS environmental microbiome revealed presence of opportunistic pathogens and antibiotic resistant microbes. Genes involved in ATP binding cassette transporters, two component systems, and beta-lactam resistance were among a diverse set of metabolic and genetic information processing pathways. Whole genome sequencing (WGS) of 50 ISS strains exhibiting resistance to various antibiotics was carried out. The antibiotic resistant genes deduced from the WGS were compared with the resistomes generated directly from the gene pool of the environmental samples. Two unique Aspergillus fumigatus strains isolated from the ISS were characterized and compared to the experimentally established clinical isolates Af293 and CEA10. A virulence assessment in a neutrophil-deficient larval zebrafish model of invasive aspergillosis indicated that both ISSFT-021 and IF1SW-F4 were significantly more lethal compared to Af293 and CEA10. The findings from this Environmental “Omics” project should be exploited to enhance human health and well-being of a closed system. In other words, the ISS-MO research aims to "translate" findings in fundamental research into medical practice (pathogen detection) and meaningful health outcomes (countermeasure development).

Perry, Jay↗
NASA NTRSDate not supplied

Sustained Veggie: A Continuous Food Production Comparison

The International Space Station’s Veggie system intermittently supplies the crew with fresh produce. To assess the potential for continuous crop production in Veggie and develop a baseline for future space crop production systems, a 120-day study was conducted to determine methodology for inputs, optimal yield, and crew involvement. ‘Amara’ mustard and ‘Red Russian’ kale were grown as initial crops, followed by ‘Extra Dwarf’ pak choi and shungiku as final crops. Previous grow-outs in Veggie have included harvests at 28-35days after initiation. In this study, a 56-day grow-out with multiple harvests from the same plants was compared to the conventional, single harvest Veggie schedule. Unlike previous Veggie studies which grew all plants simultaneously, this test staggered initiation and harvest, aiming for consistent and increased production. Plant pillows were initiated in pairs weekly and positioned to reduce shading. Completed pillows were immediately replaced with fresh ones. The multi-harvest scheme used fewer pillows, totaling 46% less pillow mass than the single harvest method. Scaled to 56-day increments, yield varied by crop and harvest scheme. ‘Red Russian’ kale yielded similarly across harvest schemes. In the multiple-harvest schedule, ‘Amara’ mustard and shungiku yielded 23% to 25% higher, respectively, while ‘Extra Dwarf’ pak choi had43% lower yield. Microbial analysis of the plants indicated no culturable human pathogens. Microbial load of a given plant appears to depend more on system age than plant age. Across harvest methods, aerobic plate counts from final crops were higher than those of initial crops. This project also considered the complexity of crew involvement in a continuous production scenario. New crew procedures that periodically remove plant material from the Veggie root mat are needed under continuous production to prevent potential pathogens and unpleasant odors. This study supports future space crop production scenarios and was funded by NASA’s Human Research Program.

Jess M Bunchek↗
NASA NTRSDate not supplied

Enterococci in Space: Adaptation, Antibiotic Resistance, and Clinical Implications

Enterococci are gram-positive bacteria that originated when our ancient animal ancestors emerged from the oceans to live on land, and brought their gut flora with them. Enterococcus faecalis (EF) and Enterococcus faecium, are common human commensals and can harbor multidrug resistance. Both have been previously isolated from the International Space Station (ISS). Likely as a consequence of their evolutionary origins, enterococci show remarkable stress resistance within, but also outside, their human hosts. Their antibiotic resistance, coupled with tolerance to desiccation, starvation, and disinfection, make some EF strains potent pathogens in the built environment (e.g., hospitals), and a potential risk to crew health during space missions. Here we describe our planned flight studies, currently in development. Genomic Enumeration of Antibiotic Resistance in Space (GEARS) will characterize the frequency and genomic identity of antibiotic resistant organisms, including enterococci, on the ISS, and expand our future in-space sequencing-based diagnostic capabilities. Enterococcus Growth Advantage on ISS via Tn-seq (EnteroGAIT) will assess the evolutionary selective pressure of the space environment (microgravity, space radiation) using EF as a model system during a long-duration culture and persistence experiment. Adaptation & Evolution of Resilient Enterococcus in Space (AERES) will study existing and newly identified isolates to characterize the “natural” evolutionary history of EF on Earth and in space to reveal mechanisms of microbial adaption including, possibly, natural selection. Our work to date has identified that EF ISS isolates share virulence factors found in clinical strains and commensals, and revealed limitations of current pathogenicity assessment tools. While ISS isolates were, in some cases, multi-drug resistant, current data suggests these are largely commensal strains. Our work will further refine potential crew health risks and improve understanding of EF adaptation to the built environment, of great relevance on Earth where EF is the second leading cause of hospital acquired infection.

Jordan McKaig↗
NASA NTRSDate not supplied

Effects of Spaceflight Relevant Carbon Dioxide Levels on Pathogenesis Related Microbial Characteristics

BACKGROUND Multiple stressors in the spaceflight environment have the potential to alter microbial pathogenesis and virulence characteristics, including microgravity, radiation, and hostile/confined conditions. This combination of factors may lead to a stacking of risks, potentially creating unexpected increase in the risk of infectious disease. One understudied variable is the higher CO2 atmospheric concentration. The current Spaceflight Maximum Allowance Concentration (SMAC) for 24-hour average CO2 is 0.4% (3 mm Hg). These elevated ambient CO2 levels aboard the ISS could potentially influence the diversity and phenotypic responses of the resident microbial communities from both the spacecraft environment (air, surface, water) and crewmembers (gut, nasal, skin microbiomes). METHODS For these studies, we will evaluate the response of Staphylococcus aureus, Streptococcus pneumoniae and Enterobacter aerogenes, which are opportunistic pathogens that are of medical significance and have been or are likely to be found aboard spacecraft. These microorganisms will be cultured in both spaceflight analog and control conditions at CO2levels representing terrestrial atmospheric concentration of 0.04% (0.3 mmHg), ISS elevated concentration of 0.4% (3 mm Hg), and an unexpected elevated concentration of 1 % (7.6 mmHg) to represent a worst case scenario. DISCUSSION Overwhelming evidence from studies performed in food microbiology, marine biology and terrestrial environmental biology suggest altered gene expression, selective bacterial inhibition, increased growth and diversity, and increased antibiotic resistance of bacterial communities individually and in biofilm formation when exposed to increased levels of CO2. While many of these findings are based on levels of CO2 higher than would be found during spaceflight exploration missions, the potential for subtle changes in CO2 levels to exacerbate infectious disease risks warrants an evaluation of pathogen responses to spaceflight CO2 conditions. Our study will provide answers to spaceflight relevant atmospheric conditions, as described in the Risk of Adverse Health Effects Due to Host-Microorganism Interaction (GapMicro 103).

A A Medina-Colorado↗
NASA NTRSDate not supplied

Effects of Spaceflight Relevant Carbon Dioxide Levels on Pathogenesis Related Microbial Characteristics

Multiple stressors in the spaceflight environment, including microgravity and radiation, have the potential to alter microbial pathogenesis and virulence characteristics. Understanding how these and other factors may exacerbate the risk of infectious disease for astronauts is important to ensure the success of the mission. One understudied question in our assessment of infectious disease risk is the contribution carbon dioxide (CO2) at atmospheric concentrations higher than would be observed in terrestrial settings. Previous terrestrial studies have shown altered gene expression, selective bacterial inhibition, increased growth and diversity, and increased antibiotic resistance of bacterial communities individually and in biofilms when exposed to increased levels of CO2. While many of these findings are based on levels of CO2 higher than would be found during spaceflight exploration missions, the potential for subtle changes in CO2 levels to exacerbate infectious disease risks warrants an evaluation of pathogen responses to spaceflight CO2 conditions. To address this question, we are evaluating the effects of elevated atmospheric CO2 on microbial concentration, diversity, and pathogenesis-related characteristics using medically significant microorganisms cultured at CO2 levels representing terrestrial atmospheric concentration (≈0.04 %), ISS elevated concentration (0.4 %), and an unexpectedly elevated concentration (1 %) to represent a worst-case scenario. To determine if microbial responses to these CO2 concentrations may be exacerbated in response to the spaceflight environment, microorganisms are being cultured under static conditions and both spaceflight analogue and control conditions using the Rotating Wall Vessel (RWV) bioreactor (Synthecon, Houston, TX). For these studies, we will evaluate the response of Staphylococcus aureus, Streptococcus pneumoniae, and Enterobacter aerogenes, which are opportunistic bacterial pathogens that are of medical significance and have been or are likely to be found aboard human-inhabited spacecraft. Bacteria will be evaluated individually and together as a consortium. The following Specific Aims are be investigated: Aim 1. Characterize the growth, diversity, and pathogenesis-related stress responses of medically significant microorganisms grown under multiple CO2 concentrations under static growth in a liquid medium. Stress responses include acid stress, oxidative stress, and thermal stress. Aim 2. Characterize the growth, diversity, and pathogenesis-related stress responses of medically significant microorganisms grown under multiple CO2 concentrations in both spaceflight analogue and control conditions. Stress responses include acid stress, oxidative stress, and thermal stress.

S G Thornhill↗
NASA NTRSDate not supplied

Rational Design of Nanoplasmonic Array Geometries for Biosensing

Background: Molecular diagnostics provide early and accurate diagnosis, which is essential for the prevention and treatment of infectious as well as chronic diseases. These tests are designed to detect disease-specific bioanalytes such as nucleic acid (DNA or RNA) or protein (antigens, antibodies) biomarkers. In the context of infectious disease diagnosis, nucleic acid-based detection methods are known to provide more specific and sensitive results. Here, the presence of a unique sequence belonging to the pathogenic genomic material is targeted to identify species, organism, genera and/or antimicrobial resistant gene markers. The majority of the common nucleic acid based diagnostic techniques require amplification (polymerase chain reaction, isothermal amplification etc.) of the pathogenic genetic material prior to detection impacting diagnostic speed, complexity, and cost thereby limiting ease of use. Thus, the development of simplified nucleic acid-based diagnostics that can be even used in resource-poor settings may hugely benefit patients across the globe. Nanopath is a molecular diagnostics company utilizing a solid-state nanosensor to enable sequence-specific detection of target nucleic acids without the need of amplification. These nanostructures enable ultra-sensitive biomarker detection using geometric, feature-dependent properties highly dependent on the local dielectric environment, allowing them to be sensitive to low concentration binding events. This paper describes an application of this approach to provide highly relevant clinical information within a single doctor’s office visit. Intro: The Nanopath team is in collaboration with NASA (National Aeronautics and Space Administration) and NIST (National Institute of Standards and Technology) to push the bounds of the fundamental physics associated with their biosensing platform. The ability of metals to support electromagnetic surface waves gives rise to surface plasmons when optically illuminated. This property, and its strong sensitivity to changes in the local refractive index, allows for the use of metal nanoparticles as ultra-sensitive transducers. In prior work by members of this team, ensembles of randomly oriented nanoparticles (i.e., colloidal nanorods dispersed on chip) were employed for sequence-specific nucleic acid sensing (1-3). While these particle sensors have the advantage of rapid fabrication, they suffer from low sensitivity and quality factor due to the random particle dispersity. In contrast, in this study we employ ordered array nanoparticle ensembles which can be used to improve sensor sensitivity and figure-of-merit. Study Methods Overview: In this talk, we detail the results of sensing experiments and computational simulations to outline a rational design of the structure of these plasmonic nanoparticle arrays for biomolecular sensing. Through simulation and experiment, we iteratively tailor nanostructure dimension to provide high quality signal and large resonance shifts upon modeled nucleic acid binding. In particular, full-wave electromagnetic simulations were conducted using Lumerical photonic simulation software in which periodic boundary conditions were applied in the x- and y- dimensions for each of the nanoplasmonic sensor geometries. To simulate the resonance response to changes in the bulk solution in contact with the sensor surface, the refractive index of the surrounding media was changed appropriately. Nucleic acid hybridization events were modeled using either using spherical structures approximating the relevant radius of genomic material as estimated by polymer models, or as conformal layers with the known refractive indices for nucleic acids. On the basis of initial simulations, nanosensors were fabricated using traditional electron-beam lithography protocols at NIST. To evaluate consensus between simulations and experiments, bulk sensing experiments were carried out in which the resonance peaks were obtained by submerging the sensors in refractive index standards. Key nanosensor characteristics including resonance peak locations, resonance peak shifts as a function of refractive index, and figure of merit (FOM) of extinction curves were examined between the experimental and simulation results prior to proceeding with simulations on additional geometries and more complex solution conditions, and further device fabrication. This iterative process is repeated toward a rational design of nanoplasmonic array geometries for biosensing optimizing response for targeted disease detection. In summary, this study puts forth a methodology for rational design and characterization of regularly spaced nanoparticle arrays for optics-based biosensing. The results of this study will allow for more informed design of nanostructure geometries towards sequence-specific nucleic acid detection. These improved designs have the potential to improve clinical sensitivity and limit-of-detection across disease indication.

sensor↗
NASA NTRSDate not supplied

Development of Machine Learning-Derived Microbiological and Immune Signatures: Applications in Adaptive Risk Assessment of Infectious Disease During Spaceflight

Infectious diseases represent an urgent risk for spaceflight with consequences ranging from loss in crew performance to crew incapacitation or loss of life should an outbreak occur. The resident environmental microbiome on the International Space Station has been monitored through routine surveillance over almost twenty years, beginning with culture-based microbial detection which has advanced to molecular methods in recent years. This has created a wealth of data that we have begun mining to define the microbial ecology of the ISS. Summarized here is our analysis of data from the historical microbial population defined by culture-based monitoring from the past two decades, organized by their likelihood to cause disease into clinical categories. As expected, many residents of the normal microflora in environments where people work and live were detected. However, some known pathogens were also detected. As the spaceflight environment can predispose humans to infection, crew health records were used to source additional data for the set to uncover clinical relevance. Data mining was performed on crew health records to capture adverse health events that may be related to infectious disease. Machine learning, specifically Random Forest analysis, was used to analyze the microbial and crew health datasets. The symptom categories were not explained by the ranked bacteria, due to lack of sufficient data for some categories and due to poor ranking of the pathogens for others. Poor ranking of the bacteria could be due to the clinical symptoms being linked to other disease-causing factors, such as allergy or viral infection. These findings suggest a lack of relationship between bacteria detected on surfaces in the ISS and historical health events experienced by astronauts.

Kristyn Hoffman↗
NASA NTRS2025

The Microbiome of A Tomato Crop Grown Under Different Lighting Regimes on the International Space Station

The VEG-05 experiment presented here investigated the effect of red-rich and blue-rich light recipes in Veggie on the microbiome of the Veggie facility and the plant tissues of a dwarf tomato variety, Solanum lycopersicum cv. Red Robin. For food safety, the plants were screened using culture-based methods for potential human pathogens that may cause infection by consumption of the fruit. The microbiome was investigated using bacterial 16S and fungal ITS sequencing methods to enumerate and identify bacterial and fungal communities on tomato fruit, roots, leaves, rooting substrate, and Veggie facility surfaces grown under blue-rich or red-rich lighting. Comparisons of microbial communities were made between lighting treatments, as well as for flight and ground controls. This analysis determined the core microbiome and microbiological composition for tomato plants grown under a blue-rich or red-rich lighting treatment and microgravity conditions. Culture-based pathogen screening, corroborated by 16S and ITS sequencing, yielded negative results. Bacterial and fungal counts were lower for ground controls than in-flight samples. However, there were no differences in microbial counts between lighting treatments. Regardless of lighting treatment, plant components shared a core microbiome, although some differences were observed in genera between lighting treatments.

Veggie↗
DOE OSTI2021

Air Return Strategies and Airborne SARS-CoV-2

Dedicated ducted air return per zone for heating, ventilating, and air-conditioning (HVAC) systems is sometimes claimed to be a superior technique over plenum return strategy from the viewpoint of exposure reduction to airborne pathogens. While both the return strategies have advantages, there is limited evidence in the literature as to which strategy is superior merely from a standpoint of building vulnerability to airborne contaminants. We performed multizonal airborne contaminant dispersion modelling using CONTAM to simulate the overall building vulnerability to airborne SARS-CoV-2 aerosols when released in an office building and evaluated the two air return strategies. Results showed that for ducted returns, maintaining negative pressure in the release zone coupled with 100% outdoor air supply can greatly reduce overall building vulnerability. However, for a building maintained under a slight positive pressure and a recirculated air percentage as low as 31%, ducted returns do not necessarily outperform plenum returns in terms of overall building vulnerability to airborne pathogens. Building-specific details and factors that are not easily represented with multizone modelling are important, making general preferential statements for either strategy difficult to make. Insights from this study can guide new construction and retrofits of buildings both during and after the COVID-19 pandemic with the aim of safe re-occupancy of buildings while keeping the buildings resilient against potential future epidemics spread by airborne agents.

Shrestha, Prateek↗
DOE OSTI2022

Chapter 8: Agricultural Use

Biochar has potential to reduce the environmental footprint in nearly every aspect of agricultural production. The use of biochar has been proposed to manage agricultural biomass (reference), to process animal manure and poultry litter (reference), to improve the nutritive value of feed (reference), and to mitigate the offsite movement of pesticides (reference) and soil nutrients (reference). The coproducts of biochar production hold similar potential. For example, on farm production of biochar can provide bioenergy to heat greenhouses and barns and to power farm equipment (reference). Pyroligneous acid, a coproduct of pyrolysis, has the ability to control fungal pathogens and deter pathogenic insects (reference). Although these environmental benefits are potentially substantial, their on-farm use has not been widely studied. Furthermore, the on-farm installation of biochar production facilities presents challenges that reduce the feasibility of co-production scenarios (Phillips 2018). As such, this section primarily addresses the application of biochar to agricultural soils.

agriculture↗
DOE OSTI2022

Lake Arrowhead Microbial Genomics Conference

The Lake Arrowhead Microbial Genomics Conference (formerly the E. coli and Small Genomes Conference, and the Microbial Genomes Conference) was held September 11-15, 2022, at the UCLA Conference Center at Lake Arrowhead, California. This conference is part of a yearly meeting initiated in 1991 to bring together genome sequencers, bioinformatics specialists, biologists, and geneticists, to forge interactions that would result in meaningful functional genomics. The goal of the meeting was to translate the influx of new genome sequencing information into useful biological studies. The Lake Arrowhead 2022 meeting had a major focus on microbial communities, the human microbiome, pathogens, phage therapy, bioinformatic methods of analysis, bioenergenics, and environmental microbiology. The field of genomics has reached the point where deriving the sequence of an organism’s entire genome is now seen as a beginning rather than an endpoint. Moreover, understanding the sequences of whole communities, and particularly those that make up different microbiomes, is now a central point of the field. The 2022 meeting covered microorganisms for which extensive analyses exist, and those for which new biological and technical strategies are being developed. The focus on biodiversity, the human microbiome, pathogenic organisms and methods of countering them, and bioenergetics added special significance to this meeting. This meeting was designed to have a mix of 36 invited presentations and poster sessions with more than 80 posters, and had 147 participants. This meeting has become established as a major annual microbial genomics conference during the last 29 years and has assured status and quality. It offers a major opportunity for young investigators and students to attend, present posters, and give talks. In fact, 50% of the invited speakers were early career stage scientists. An important impact of the conference was the forging of new collaborations between scientists and particularly young scientists with the array of multidisciplinary researchers at the meeting.

Miller, Jeffrey H.↗
DOE OSTI2025

Exploring phage–host interactions in Burkholderia cepacia complex bacterium to reveal host factors and phage resistance genes using CRISPRi functional genomics and transcriptomics

Complex interactions of bacteriophages with their bacterial hosts determine phage host range and infectivity. While phage defense systems and host factors have been identified in model bacteria, they remain challenging to predict in non-model bacteria. In this paper, we integrate functional genomics and transcriptomics to investigate phage–host interactions, revealing active phage resistance and host factor genes in Burkholderia cenocepacia K56-2. Burkholderia cepacia complex species are commonly found in soil and are opportunistic pathogens in immunocompromised patients. We studied infection of B. cenocepacia K56-2 with Bcep176, a temperate phage isolated from Burkholderia multivorans. A genome-wide dCas9 knockdown library targeting B. cenocepacia K56-2 was constructed, and a pooled infection experiment identified 63 novel genes or operons coding for candidate host factors or phage resistance genes. The activities of a subset of candidate host factor and resistance genes were validated via single-gene knockdowns. Transcriptomics of B. cenocepacia K56-2 during Bcep176 infection revealed that expression of genes coding for host factor and resistance candidates identified in this screen was significantly altered during infection by 4 h post-infection. Identifying which bacterial genes are involved in phage infection is important to understand the ecological niches of B. cenocepacia and its phages, and for designing phage therapies.

Bacterial Pathogenesis↗
DOE OSTI2026

An improved dataset for predicting mammal infecting viruses from genetic sequence information

There have been several attempts to develop machine learning (ML) models to identify human infecting viruses from their genomic sequences, with varying degrees of success. Direct comparison between models is problematic, because these models are typically trained and evaluated on different datasets with alternative data splitting schemes, features, and model performance metrics. In this paper we present a standardized dataset of mammal infecting and non-infecting viral pathogens, refined from the previous work of Mollentze et al. to include the latest literature evidence, roughly doubling the number of curated host-virus records available to the community, and new host target labels, primate and mammal. The new host labels were included for several reasons, including previous reports that classification performance is better at broader taxonomic ranks and the idea that there may be more data for primate infection that might serve as a suitable proxy for zoonotic potential and avoidance of false positives for human infection due to absence of evidence. On this dataset, we report the performance of eight machine learning models for predicting mammal-infecting viruses from their genomic sequences. We find that randomly assigning cases in our improved dataset to training/testing sets, when compared to the original assignments into training/testing in Mollentze et al., increases the overall average ROC AUC of prediction of human infection from 0.663 ± 0.070 to 0.784 ± 0.013, consistent with the reduction in phylogenetic distance between train and test sets (relative entropy change from 3.00 to 0.08). The broadest host category of mammal infection can be predicted most reliably at 0.850 ± 0.020. We share our improved dataset and code to enable standardized comparisons of machine learning methods to predict human host infections. Overall, we have presented preliminary evidence that classification of virus host infection is more tractable at higher taxonomic ranks, that unsurprisingly reducing the phylogenetic distance between training and test sets can improve predictive performance, that peptide kmer features appear to be harmful to out of sample model performance, and we are left with the question of whether models for virus host prediction can reasonably be expected to perform well in out of sample scenarios given the likelihood that viruses do not share a common ancestor. Consistent with this concern, when the data is resampled such that there is no overlap between viral families in training and test sets (relative entropy > 24), models perform no better than random chance at prediction of human infection regardless of whether kmers are included (ROC AUC 0.50 ± 0.08) or not (ROC AUC 0.50 ± 0.04).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Visualizing and analyzing 3D biomolecular structures using Mol* at RCSB.org: Influenza A H5N1 virus proteome case study

The easiest and often most useful way to work with experimentally determined or computationally predicted structures of biomolecules is by viewing their three-dimensional (3D) shapes using a molecular visualization tool. Mol* was collaboratively developed by RCSB Protein Data Bank (RCSB PDB, RCSB.org) and Protein Data Bank in Europe (PDBe, PDBe.org) as an open-source, web-based, 3D visualization software suite for examination and analyses of biostructures. It is capable of displaying atomic coordinates and related experimental data of biomolecular structures together with a variety of annotations, facilitating basic and applied research, training, education, and information dissemination. Across RCSB.org, the RCSB PDB research-focused web portal, Mol* has been implemented to support single-mouse-click atomic-level visualization of biomolecules (e.g., proteins, nucleic acids, carbohydrates) with bound cofactors, small-molecule ligands, ions, water molecules, or other macromolecules. RCSB.org Mol* can seamlessly display 3D structures from various sources, allowing structure interrogation, superimposition, and comparison. Using influenza A H5N1 virus as a topical case study of an important pathogen, we exemplify how Mol* has been embedded within various RCSB.org tools—allowing users to view polymer sequence and structure-based annotations integrated from trusted bioinformatics data resources, assess patterns and trends in groups of structures, and view structures of any size and compositional complexity. In addition to being linked to every experimentally determined biostructure and Computed Structure Model made available at RCSB.org, Standalone Mol* is freely available for visualizing any atomic-level or multi-scale biostructure at rcsb.org/3d-view.

3D biostructure↗
DOE OSTI2022

Bayesian time-varying occupancy model for West Nile virus in Ontario, Canada

Occupancy models determine the true presence or absence of a species by adjusting for imperfect detection in surveys. They often assume that species presences can be detected only if sites are occupied during a sampling season. We extended these models to estimate occupancy rates that vary throughout a sampling season as well as account for spatial dependence among sites. For these methods, we constructed a fast Gibbs sampler with the Pólya-Gamma augmentation strategy to conduct inference on covariate effects. We applied these methods to evaluate how environmental conditions and surveillance practices are associated with the presence of West Nile virus in mosquito traps across Ontario, Canada from 2002 to 2017. We found that urban land cover and warm temperatures drove viral occupancy, whereas viral testing on pools with higher proportions of Culex mosquitoes was more likely to result in a positive test for West Nile virus. Models with time-varying occupancy effects achieved much lower Watanabe-Akaike information criteria than models without such effects. Our final model had strong predictive performance on test data that included some of the most extreme seasons, demonstrating the promise of these methods in the study of pathogens spread by mosquito vectors.

60 APPLIED LIFE SCIENCES↗