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At least 145 records · Page 8

A Comprehensive Characterization of Microorganisms and Allergens in Spacecraft Environment

The determination of risk from infectious disease during long-duration missions is composed of several factors including the concentration and the characteristics of the infectious agent. Thus, a thorough knowledge of the microorganisms aboard spacecraft is essential in mitigating infectious disease risk to the crew. While stringent steps are taken to minimize the transfer of potential pathogens to spacecraft, several medically significant organisms have been isolated from both the Mir and International Space Station (ISS). Historically, the method for isolation and identification of microorganisms from spacecraft environmental samples depended upon their growth on culture media. Unfortunately, only a fraction of the organisms may grow on a culture medium, potentially omitting those microorganisms whose nutritional and physical requirements for growth are not met. Thus, several pathogens may not have been detected, such as Legionella pneumophila, the etiological agent of Legionnaire s disease. We hypothesize that environmental analysis using non-culture-based technologies will reveal microorganisms, allergens, and microbial toxins not previously reported in spacecraft, allowing for a more complete health assessment. The development of techniques for this flight experiment, operationally named SWAB, has already provided advances in NASA laboratory processes and beneficial information toward human health risk assessment. The translation of 16S ribosomal DNA sequencing for the identification of bacteria from the SWAB experiment to nominal operations has increased bacterial speciation of environmental isolates from previous flights three fold compared to previous conventional methodology. The incorporation of molecular-based DNA fingerprinting using repetitive sequence-based polymerase chain reaction (rep-PCR) into the capabilities of the laboratory has provided a methodology to track microorganisms between crewmembers and their environment. Both 16S ribosomal DNA identification and bacterial fingerprinting have improved NASA s capability to better understand spacecraft environments and determine the source of contamination events. Preflight sampling has been completed for air, surface, and water samples. In-flight sample collection has been completed for a total of 8 air and surface sample collection sessions. In-flight hardware has performed well and the surface sampling device received positive feedback from the crew for its ease of use. While processing and analysis continue for these samples, early results have begun to provide information on the spacecraft environment. Using a method called Denaturing Gradient Gel Electrophoresis (DGGE), several air and samples were evaluated to determine the types of organisms that were present. Using only molecular techniques, DGGE does not depend on any microbial growth on culture media, allowing a more comprehensive assessment of the spacecraft interior. Preliminary results have identified several microorganisms that would not have been isolated using current technology, though none of these organisms would be considered medically significant. Interestingly, the isolation of Gram negative organisms is greater using DGGE than conventional media based isolation. The cause of this finding is unclear, though it may be the result of the technique s ability to isolate both viable and non-viable bacteria. The next phase of the SWAB sample analysis is the use of quantitative polymerase chain reaction (QPCR) to look for specific medically significant organisms. While not as broad as DGGE, QPCR is much more sensitive and may reveal findings that were not seen during the initial evaluation. Together, this information will lead toward an accurate microbial risk assessment to help set flight requirements to protect the safety, health, and performance of the crew.

Castro, V.A.↗

Discrimination of Spore-Forming Bacilli Using spoIVA

A method of discriminating between spore-forming and non-spore-forming bacteria is based on a combination of simultaneous sporulation-specific and non-sporulation-specific quantitative polymerase chain reactions (Q-PCRs). The method was invented partly in response to the observation that for the purposes of preventing or reducing biological contamination affecting many human endeavors, ultimately, only the spore-forming portions of bacterial populations are the ones that are problematic (or, at least, more problematic than are the non-spore-forming portions). In some environments, spore-forming bacteria constitute small fractions of the total bacterial populations. The use of sporulation-specific primers in Q-PCR affords the ability to assess the spore-forming fraction of a bacterial population present in an environment of interest. This assessment can provide a more thorough and accurate understanding of the bacterial contamination in the environment, thereby making it possible to focus contamination- testing, contamination-prevention, sterilization, and decontamination resources more economically and efficiently. The method includes the use of sporulation-specific primers in the form of designed, optimized deoxyribonucleic acid (DNA) oligonucleotides specific for the bacterial spoIVA gene (see table). [In "spoIVA," "IV" signifies Roman numeral four and the entire quoted name refers to gene A for the fourth stage of sporulation.] These primers are mixed into a PCR cocktail with a given sample of bacterial cells. A control PCR cocktail into which are mixed universal 16S rRNA primers is also prepared. ["16S rRNA" denotes a ribosomal ribonucleic acid (rRNA) sequence that is common to all organisms.] Following several cycles of heating and cooling according to the PCR protocol to amplify amounts of DNA molecules, the amplification products can be analyzed to determine the types of bacterial cells present within the samples. If the amplification product is strong, relative to the product of a control PCR sequence, then it is concluded that the bacterial population in the sample consists predominantly of spore-forming cells. If the amplification product is weak or nonexistent, then it is concluded that the bacterial population in the sample consists predominantly or entirely of non-spore-forming cells.

Venkateswaran, Kasthuri↗

Study of the Microbial Diversity of a Newly Discovered East Antarctic Freshwater Lake, L27C, and of a Perennially Ice-Covered Lake Untersee

The microbial communities that reside within freshwater lakes of Schirmacher and Untersee Oases in East Antarctica must cope with extreme conditions that may include cold temperature, annual freeze-thaw cycles, exposure to UV radiation, especially during the austral summer months, low light beneath thick ice-cover, followed by seasonal darkness. The objective of this study was to assess the microbial biodiversity and distribution from samples taken from two freshwater lakes (L27C and Lake Untersee) that were collected during the Tawani 2008 International Antarctic Expedition that conducted research in this region of Antarctica. L27C is a small, previously unreported lake residing 2 km WNW of Maitri Station at Schirmacher Oasis. Biodiversity and distribution of microorganisms within the lake were studied using both culture-independent and culture-dependent methodologies based upon the analysis of eubacterial 16S rRNA gene sequences. Lake Untersee, a perennially ice-covered, ultra-oligotrophic, lake in the Otto-von-Gruber-Gebirge (Gruber Mountains) of central Dronning Maud Land was also sampled and the microbial diversity was analyzed by eubacterial 16S rRNA gene sequences derived from pure cultures. Direct culturing of water samples from each lake on separate R2A growth medium exhibited a variety of microorganisms including: Janthinobacterium, Hymenobacter, Sphingamonas, Subtercola, Deinococcus, Arthrobacter, Flavobacterium, Polaromonas, Rhodoferax and Duganella. The evaluation of samples from L27C through culture-independent methodology identified a rich microbial diversity consisting of six different phyla of bacteria. The culture-independent analysis also displayed the majority of bacteria (56%) belonged to the Class gamma-proteobacteria within the phylum Proteobacteria. Within the Class gamma-proteobacteria, Acinetobacter dominated (48%) the total microbial load. Overall, L27C exhibited 7 different phyla of bacteria and 20 different genera. Statistical analysis (Shannon-Weaver Diversity Index and Simpson Diversity Index) of the biodiversity of L27C displayed a moderately rich and diverse community. Investigations of the biodiversity and distribution of microorganisms in these lakes will help further our understanding of how the physical environment impact the structure and function within these microbially dominated ecosystems.

Huang, Jonathan P.↗

Microbial Lipid and C Isotopic Biosignatures of a Unique Community at Grand Prismatic Spring, Yellowstone National Park

The microbial communities found in modern hot springs are considered analogs to ones that may have existed in hydrothermal systems on the early Earth and possibly Mars. Our goal was to characterize the microbial biosignatures and to assess the preservation of organic matter in the silica-depositing Grand Prismatic Spring in Yellowstone National Park. This study combines 16S rRNA surveys, lipid biomarkers, and C isotopes to query, "Who's there and what are they doing?" On the edge of the approximately 90 m diameter blue vent pool (56.1 C, pH 8.5), a floating green streamer community grew over a benthic pink community. The membrane lipids in the green streamers and pink mat were composed of unusual ester-linked fatty acids, indicating the presence of novel bacterial groups. In particular, we discovered a series of 2-methyl and 2,X-dimethyl phospholipid fatty acids (C18-22). We are attempting to use the 16S rRNA surveys to link these compounds to source organisms. Wax esters, biomarkers for Chloroflexi, were present in both communities, but displayed different profiles. A higher proportion of branched wax esters were found in the green streamers, and were associated with a relatively high concentration of long-chain di- and trienes (C29-31). This suggests that Chloroflexus primarily grew in the green streamers, while a pink mat of Roseiflexus grew on the sinter substrate underneath. Cyanobacterial alkanes were found in the green streamers (n-C17, 7-, 6- and 5-monomethyl-C17, 7,11-dimethyl-C17, n-C19, n-C19:1). We also detected a series of monoalkylglycerylethers and geologically relevant hopanoids in both communities. Carbon isotope analyses indicated that Chloroflexus was growing photoheterotrophically using cyanobacterial photosynthate. Roseiflexus also traditionally grows photoheterotrophically, but the C isotopic signatures of the lipids in the pink mat were approximately 10 %0 lighter than the cyanobacterial and Chloroflexus lipids, indicating a potentially novel metabolic mechanism or prior secondary reworking of substrates before reaching Roseiflexus. This arrangement of Synechococcus-Chloroflexus green streamers floating over a benthic pink community of Roseiflexus is different from the classical laminated Synechococcus-Chloroflexi mats at Octopus Hot Spring.

Jahnke, Linda↗

Genotypic & Phenotypic Diversity of Microbial Isolates from the Mars Exploration Rovers

Mars-bound rovers such as the Mars Exploration Rover (MER) endure strict planetary protection implementation campaigns to assess bioburden. The objective of this study is to identify cultivable microorganisms isolated by the NASA Standard Assay from spacecraft during pre-launch and evaluate their potential to survive conditions on the Martian surface. Of approximately 350 isolates collected from the MER spacecraft archive, 171 microorganisms were reconstituted for characterization via 16S rRNA fingerprinting. Alignment of 16S sequences revealed high levels of sequence similarity to spore-forming species, overwhelmingly of the genera Bacillus (73.7%) and Paenibacillus (14.0%). Samples underwent phenotype characterization employing multiple carbon sources and ion concentrations in an automated microarray format using the Omnilog system. Working and stock cultures were prepared to address the immediate needs for day-to-day culture utilization and long-term preservation, respectively. Results from this study produced details about the microbes that contaminate surfaces of spacecraft, as well as a preliminary evaluation of a rapid biochemical ID method that also provides a phenotypic assessment of contaminants. The overall outcome of this study will benefit emerging cleaning and sterilization technologies for preventing forward contamination that could negatively impact future life detection or sample return missions.

Mars Exploration Rovers↗

Seed surface sanitization and persistence of E.coli through different tissues of ‘Red Robin’ Tomato (Solanum lycopersicum cv. Red Robin)

Seed surface sanitization via chemical processes removes/reduces microbes from the external surfaces of the seed and thereby could have an impact on the plants’ health or productivity. To determine the impact of seed surface sanitization on the plants’ microbiome, sanitized and unsanitized seeds from ‘Red Robin’ Tomato (Solanumlycopersicum cv. Red Robin) were exposed to Escherichia coli (E. coli) and grown in a controlled environment growth chamber simulating environmental conditions aboard the International Space Station (ISS). Plants were harvested at four intervals, days 11, 33,42 and 76 post-germination. Changes in the microbial communities of leaf, stem, root, and fruit because of E. coli exposure and the persistence of E. coli itself were investigated using aerobic plate count (APC), qPCR and 16S rRNA sequencing. It was determined that E. coli persisted for longer periods of time in plants from sanitized versus unsanitized seeds and was identified in root tissue more frequently than in leaf or stem tissue. E. coli was not detected in fruits raised from either sanitized or unsanitized seeds. The 16S rRNA sequencing showed dynamic changes in the abundance of members of the phylum Proteobacteria, Bacteroidetes, Actinobacteria, and Firmicutes in all tissue types studied. We observed minimal or no changes in the alpha diversity of leaf stem and fruit tissue with time, or between sanitized and unsanitized seeds. Roots showed significant differences in alpha diversity with time and seed sanitization status. Beta-diversity showed that time had more of an influence on all samples versus the E. coli treatment. Members of phyla Proteobacteria and Bacteroidetes were found to be differentially abundant across leaf, stem and root tissue. Our results indicated that the seed surface sanitization, although a requirement for sending seeds to space, might influence the developing microbiome. This research was funded by NASA’s Space Life and Physical Sciences Research and Applications.

Anirudha R Dixit↗

Microbial Monitoring of New Cleanrooms Used to Curate Astrobiologically Relevant Asteroid Samples from Bennu and Ryugu

Introduction: NASA has constructed two new cleanrooms to house materials from the OSRIS-REx and Hayabusa2 missions to the asteroids Ryugu (162173) and Bennu (101955), respectively. In accordance with standard astromaterials curation practices, these cleanrooms will be monitored for particulate contamination and maintained to ISO 5 equivalent standards1. Since the samples in these collections are expected to contain prebiotic organic compounds that may help explain the origin of life on Earth, these labs will also be monitored for organic and biological contamination2. Samples from Ryugu arrived on Earth in December, 2020. After basic characterization in Japan, NASA received a subset of these samples at the astromaterials curation facility in Houston in December of 2021. OSIRIS-REx is expected to return samples in September, 2023. Here we present preliminary microbial monitoring results from monthly monitoring of these new labs and the connected microtomy and staging areas that support them, as they are being commissioned. We also compare these results to baseline values for other astromaterials curation labs. We will also briefly describe additional cleaning efforts employed to reduce the bioburden in these new cleanrooms. Methods: Microbial samples were collected from surfaces using a dry macrofoam swab (Puritan Brand 2518051PFRNDFD). Swabs were also opened in the lab but not touched to any surfaces to function as negative controls. Samples and controls were processed inside a class II biosafety cabinet to avoid inadvertent cross contamination. The swabs were suspended in 15 ml of PBS (Phosphate Buffered Saline) and vortexed for 20 seconds to remove cells from the swab surface. The PBS was used to inoculate Petri dishes filled with TSA (Tryptic Soy Agar), Blood Agar, or Reasoners 2 agar to check for microbial growth. Each plate was inoculated with 0.1 ml of PBS. The TSA and blood agar plates were incubated at 35˚C and the Reasoners 2 agar plates were incubated at 25˚C for seven days. Petri dishes filled with Potato dextrose agar, Saboraud dextrose agar, or Saboraud dextrose agar with 0.1 mg/ml of chloramphenicol, an antibiotic, were used to check for fungal growth. These plates were inoculated with 0.3 ml of PBS and incubated at 30˚C. The remaining PBS was frozen at -80 ˚C for DNA sequencing. After incubation, isolates were counted and reisolated for identification. Isolates were identified using the VITEK23 system or by sequencing a portion of the 16S rRNA gene for bacteria or the ribosomal internal transcribed spacer (ITS) for fungi. Sequencing was performed with an ABI 3500 Sanger sequencer. Results: During our initial sampling, six of the seven sites sampled (86%) displayed bacterial or fungal growth. Samples collected from the staging areas and microtomy labs are not included in this calculation since those areas are maintained at a lower ISO 7 equivalent cleanliness standard. A month later, only three of the seven sites (43%) displayed bacterial growth. No fungal growth was detected in the second sampling. Since new equipment had been introduced to the Hayabusa2 lab since the first round of sampling, an additional three sampling sites were included in the second round of sampling. None of these sites displayed microbial growth. These sites will be included in all future sampling efforts. Bacterial isolates have been identified from the following genera at multiple time points: Micrococcus, Staphylococcus, and Bacillus. Isolates from the genera: Microbacterium, Nocardioides, Methylocystis, and Microvirga were identified in the initial sampling, but were not present at later time points. Identification of fungal isolates is in progress. Results are summarized in Table 1. Discussion: The recovery rate or percentage of positive samples4 was initially 86%, which is higher than the median recovery rate for comparable ISO 5 equivalent curation labs like Stardust (33%), Hayabusa (33%), and Cosmic Dust (50%). However, after a month of operation, the recovery rate for these same sites decreased to 43%, which is similar to what we observe in comparable curation cleanrooms with no microbial control requirements. Adding in the new sampling sites further decreases the recovery rate to 30%. With the reduction in recovery rate, we also observed a decrease in microbial diversity. At the first time point, we observed at least 10 different bacterial species and at least two different fungi. This is a higher diversity than the median values for comparable ISO 5 equivalent labs (2-4 isolates per sampling event). After the second sampling, we observed at least 4 bacterial species and no fungi, which is more consistent with comparable labs. We expect the recovery rate and diversity in both labs to continue to decrease as routine operation continues. We will use ultrapure hydrogen peroxide to disinfect equipment and work areas prior to opening any sample containers. Most of the bacterial and fungal isolates were detected on samples from the cleanroom floors. This is consistent with baseline results from other curation labs. Organisms from the genera Bacillus, Staphylococcus, and Micrococcus that were repeatedly detected are common in cleanrooms and on human skin5,6. These organisms are generally thought to be introduced when people enter the cleanroom. Microbacterium, Nocardioides, and Microvirga have also previously been identified in astromaterials cleanrooms, but not as frequently as Bacillus, Staphylococcus, and Micrococcus. Methylocystis is a novel genus in the astromaterials cleanrooms, but it was identified with low accuracy (93% match in the sequenced region of the 16S rRNA gene) and further work is needed to confirm this identification. Microbacterium is a diverse genus with isolates identified from terrestrial and aquatic sediments. Some species of Microbacterium are capable of degrading complex organic compounds found in crude oil. The presence of these bacteria in the OSIRIS REx and Hayabusa2 cleanrooms should be closely monitored. Methylocystis is a genus of methanotrophic bacteria capable of oxidizing methane. If this identification proves to be correct and it is detected again, it should be closely monitored as well. Under nominal operating conditions, samples should not ever encounter the cleanroom floor or other high traffic areas. If we observe an increase in the bioburden in sensitive work areas that appears to be influenced by organism transfer from high traffic areas like the floors, we can employ additional hydrogen peroxide treatments to disinfect high traffic areas. Routine microbial monitoring of these labs will ensure that NASA’s astromaterials collections remain pristine and useful for scientific study. Table 1. Sampling Locations and Colony Counts Bacterial CFUa Fungal CFU Bacterial CFU Fungal CFU Lab - Location 11/2/2021 11/2/2021 12/13/2021 12/13/2021 H2b-Floor 4 8 1 0 H2-staging pass through 3 0 0 0 H2-microtomy pass through TNTCc 0 0 0 H2 Microscope 1 NA NA 0 0 H2 Microscope 2 NA NA 0 0 H2-Table NA NA 0 0 OREXd- microtomy pass through 0 0 6 0 OREX – Anteroom pass through 0 0 0 0 OREX – Floor 1 2 0 0 OREX Witness Foil Table 3 0 1 0 Staging-Floor 16 0 15 0 Microtomy-Floor 3 0 2 0 a: CFU = Colony Forming Unit b: H2 = Hayabusa2 Lab c: TNTC = too numerous to count d: OREX = OSIRIS-REx Lab References: 1. ISO 14644-1:2015 - Cleanrooms and associated controlled environments -- Part 1: Classification of air cleanliness by particle concentration. 37 (2015). 2. McCubbin, F. M. et al. Space Sci Rev 215, (2019). 3. Pincus, D. H. Encyclopedia of Rapid Microbiological Methods (2005). 4. The United States Pharmacopeial Convention. USP General Chapter <1116> 17, 784–794 (2013). 5. Sheraba, N. S., Yassin, A. S. & Amin, M. BMC Research Notes 3, 278 (2010). 6. Utescher, C. L. de A., Franzolin, M. R., Trabulsi, L. R. & Gambale, V. Brazilian Journal of Microbiology 38, 710–716 (2007).

A B Regberg↗

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↗

Thirdhand cigarette smoke leads to age-dependent and persistent alterations in the cecal microbiome of mice

The gut microbiome composition is influenced by many factors including environmental exposures. Here, we investigated the effect of thirdhand cigarette smoke (THS) and exposure age on gut microbiome diversity. C57BL/6 mice were exposed to THS at human exposure relevant levels for three weeks during three different life stages: postnatal (0–3 weeks of age), pubescent (4–7 weeks of age), and adult (9–12 weeks of age), respectively. Cecal microbiome profiles were assessed at 17 weeks of age by 16S rRNA gene sequencing. We found that age at THS exposure strongly influenced the cecal microbiome composition. Although postnatal THS exposure significantly influenced the microbial composition, pubescent and adulthood exposures only had minor effects. The microbiome of postnatally THS-exposed mice significantly increased several degradation pathways that regulate glycolysis and pyruvate decarboxylation, and significantly decreased coenzyme A biosynthesis and pyrimidine deoxyribonucleoside salvage. Our results indicate that mouse postnatal development is particularly susceptible to persistent THS exposure effects on the gut microbiome.

16S rRNA gene sequencing↗

DISCOVR strain pipeline screening – Part I: Maximum specific growth rate as a function of temperature and salinity for 38 candidate microalgae for biofuels production

Here, to identify high productivity strains for microalgal biofuels generation, the maximum specific growth rate of 38 strains was measured as a function of salinity (i.e., 5, 15, and 35 PSU) and temperature (i.e., at 8 temperatures along a linear gradient from ca. 5 to 45°C) to determine the most suitable growth medium salinity and best growing season, respectively, for outdoor raceway pond cultivation. The following strains were evaluated: Agmenellum quadruplicatum UTEX 2268, Anabaena sp. ATCC 33081, Arthrospira fusiformis UTEX 2721, Arthrospira platensis UTEX 3086, Chlorella vulgaris NREL 4-C12, Chlorella autotrophica CCMP 243, Chlorella sorokiniana DOE1044, Chlorella sorokiniana DOE 1116, Chlorella sorokiniana DOE 1412 (UTEXB3016), Chlorella vulgaris LRB AZ-1201, Chlorococcum littorale UTEX 117, Chlorococcum sp. UTEX-B P7, Chloromonas reticulata CCALA 870, Coelastrella sp. DOE 0202, Cyanobacterium sp. AB1, Micractinium reisseri NREL 14-F2, Microchloropsis gaditana CCMP1894, Microchloropsis salina CCMP 1776, Monoraphidium sp. MONOR1, Monoraphidium minutum 26B-AM, Nannochloropsis oceanica CCAP 849/10, Oscillatoria cf. priestleyi CCMEE 5020.1-1, Picochlorum celeri TG2-WT-CSM/EMRE, Picochlorum oklahomensis CCMP 2329, Picochlorum renovo NREL 39-A8, Picochlorum soloecismus DOE 101, Porphyridium cruentum CCMP 675, Scenedesmus acutus LRB-AP-0401, Scenedesmus obliquus DOE 0152.z, Scenedesmus obliquus UTEX393,Scenedesmus rubescens NREL 46B-D3, Scenedesmus sp. IITRIND2, Stichococcus minor CCMP 819, Stichococcus minutus CCALA 727, Synechococcus elongatus UTEX2973.1, Tetraselmis striata LANL 1001, Tisochrysis lutea CCMP 1324, and Tribonema minus UTEXB3156. For each strain, the identity and the presence of bacterial cohorts was determined using 18S and 16S rDNA sequencing, respectively. The maximum specific growth rate versus temperature data were also used to determine the activation energies (Arrhenius equation) for most strains. For all strains, the measured salinity and temperature tolerance data were compared to those reported in the literature. The fastest growing strains were down-selected for subsequent biomass productivity measurements in climate-simulation photobioreactors, as reported in the next paper in the issue.

18S and 16S rDNA sequencing↗

Impact of sanitizer application on Salmonella mitigation and microbiome shift on diced tomato during washing and storage

Here, this study examined the shift in Salmonella and indigenous microbiota on diced tomato after washing with three different sanitizers and during post-wash cold storage. Roma tomatoes were inoculated with a Salmonella cocktail (initial level ~ 5.6 CFU g –1 ), diced and washed along with uninoculated diced tomato in simulated flume wash water with sanitizers, including 10 mg L –1 free chlorine (FC10), 90 mg L –1 peracetic acid (PA90), PA90 in combination with a proprietary acidified surfactant blend (PS90), and unsanitized control (CK). Salmonella, total mesophilic aerobic bacteria (MAB), and yeast and mold (YM) populations on both inoculated and uninoculated samples were measured before and after washing, and during storage at 4 °C. 16S rRNA amplicon sequencing was also performed to determine the shift in bacterial microbiome. Washing with all tested sanitizers, especially PS90 (> 4 log Salmonella reduction), reduced Salmonella and MAB populations on inoculated diced tomatoes. Additionally, application of sanitizers mitigated Salmonella cross-contamination onto uninoculated samples. PS90 treatment inhibited the proliferation of most dominant bacteria on diced tomatoes during storage, including Erwiniaceae, Curtobacterium, Pantoea, Erwinia and Enterobacterales, which may benefit product quality and safety.

16S rRNA amplicon sequencing↗

The effects of warming and soil chemistry on bacterial community structure in Arctic tundra soils

Soil microbial communities in the Arctic play a critical role in regulating the global carbon (C) cycle. Vast amounts of C are stored in northern latitude soils, and rising temperatures in the Arctic threaten to thaw permafrost, making relatively inaccessible C sources more available for mineralization by soil microbes. Few studies have characterized how microbial community structure responds to thawing permafrost in the context of varying soil chemistries associated with contrasting tundra landscapes. Here, we subjected active layer and permafrost soils from upland and lowland tundra sites on the North Slope of Alaska to a soil-warming incubation experiment and compared soil bacterial community profiles (obtained by 16S rRNA amplicon sequencing) before and after incubation. The influence of soil composition (characterized by mid-infrared [MIR] spectroscopy) on bacterial community structure and class abundance was analyzed using redundancy and correlation analyses. We found increased abundances of Alphaproteobacteria, Gammaproteobacteria, and Bacteroidetes [Sphingobacteriia] post incubation, particularly in permafrost soils. The categorical descriptors site and soil layer had the most explanatory power in our predictive models of bacterial community structure, highlighting the close relationship between soil bacteria and the soil environment. Specific soil chemical attributes characterizing the soil environments that were found to be the best predictors include MIR spectral bands associated with inorganic C, silicates, amide II (C=N stretch), and carboxylics (C-O stretch), and MIR peak ratios representing C substrate quality. Overall, these results further characterize soil bacterial community shifts that may occur as a frozen environment with limited access to C sources, such as is found in undisturbed permafrost, transitions to warmer and more C-available environments, such as is predicted in thawing permafrost due to climate change.

16S rRNA sequencing↗

Predicting measures of soil health using the microbiome and supervised machine learning

Soil health encompasses a range of biological, chemical, and physical soil properties that sustain the commercial and ecological value of agroecosystems. Monitoring soil health requires a comprehensive set of diagnostics that can be cost-prohibitive for routine analyses. The soil microbiome provides a rich source of information about soil properties, which can be assayed in a high-throughput, cost-effective way. We evaluated the accuracy of random forest (RF) and support vector machine (SVM) regression and classification models in predicting 12 measures of soil health, tillage status, and soil texture from 16S rRNA gene amplicon data with an operationally relevant sample set. We validated the efficacy of the best performing models against independent datasets and also tested best practices for processing microbiome data for use in machine learning. Soil health metrics could be predicted from microbiome data with the best models achieving a Kappa value of ~0.65, for categorical assessments, and a R2 value of ~0.8, for numerical scores. Biological health ratings were better predicted than chemical or physical ratings. Validation with independent datasets revealed that models had general predictive value for soil properties, including yield. The ecological profiles of several taxa important for model accuracy matched the observed relationships with soil health, including Pyrinomonadaceae, Nitrososphaeraceae, and Candidatus Udeaobacter. Models trained at the highest taxonomic resolution proved most accurate, with losses in accuracy resulting from rarefying, sparsity filtering, and aggregating at higher taxonomic ranks. Furthermore, our study provides the groundwork for developing scalable technology to use microbiome-based diagnostics for the assessment of soil health.

16S rRNA gene↗

Seeding Advanced Treated Wastewater for Purposes of Direct Potable Reuse

Direct potable reuse (DPR) is a promising solution to address water scarcity. However, a better understanding of how introducing advanced treated water (ATW) affects microbial communities present in distribution systems is needed. Here, in this study, we measured changes to the microbial water quality in simulated distribution systems that were conditioned using treated, unimpaired surface water (SW) and then transitioned to ATW. In addition, we investigated whether adding a biological filtration step would seed the microbial community of the ATW and whether the influence would persist in the simulated distribution systems. We found that the bulk water in the ATW-fed distribution systems had lower cell counts and ATP concentrations and a distinct microbial community (based on 16S amplicon sequencing) compared to the SW-fed or the seeded ATW-fed systems. However, biofilm community composition and biomass remained consistent regardless of the feedwater. Increased microbial biomass and diversity were present in the seeded ATW, with several amplicon sequence variants identified as being introduced by the biological filter. Our results suggest that directly introducing ATW to distribution systems could disturb the existing microbial community. Preparing ATW for distribution via biological filtration may deliver more predictable and stable microbial water quality than introducing unseeded ATW.

16S↗

Assessing biogeographic survey gaps in bacterial diversity knowledge: A global synthesis of freshwaters

Freshwaters account for 0.8% of Earth's surface area, yet support >10% of known plant and animal species making them disproportionately biodiverse. Modern molecular techniques have begun to reveal microbial diversity, but application of these approaches to address global microbial biogeography is relatively unknown in freshwaters. Our aim was to identify gaps in microbial data coverage along climatic and landscape disturbance gradients and among terrestrial biomes and hydrographic regions for all freshwater ecosystems and three freshwater habitat types: lakes and reservoirs (lentic); streams and rivers (lotic); and wetlands. We reviewed literature on microbial diversity in freshwaters surveyed using 16S ribosomal RNA sequencing which identify microbial taxa. We georeferenced survey locations and used a geographic information system to identify and map gaps in survey coverage using open-source data for climate, landscape disturbance, terrestrial biomes, and freshwater ecoregions. In our study, we compiled 3,425 georeferenced survey locations reported from 963 studies. Streams were surveyed most frequently (60.8% of survey locations), followed by lakes (33.5%) and wetlands (5.6%). Surveys were concentrated in North America, central and western Europe, and Southeast Asia; 35% of freshwater ecoregions were surveyed at least once across freshwater habitat types, whereas 23%, 23%, and 12% were surveyed at least once for lentic, lotic, and wetland habitat types, respectively. The climatic gap analysis indicated coverage is high for temperate regions but lacking in the tropics and Arctic, particularly for wetland ecosystems. Our assessment revealed high climatic coverage of freshwater microbial diversity knowledge, but expansive ecoregional gaps attributable to biased sampling near research institutions in North America, western Europe, and China. Future surveys should target ecoregions in Africa, South America, Central Asia, Australia, and Antarctica. An essential next step will be to curate and disseminate sequencing efforts to facilitate the study of processes driving global diversity patterns.

16S rRNA↗

Soil Origin and Plant Genotype Modulate Switchgrass Aboveground Productivity and Root Microbiome Assembly

Switchgrass (Panicum virgatum) is a model perennial grass for bioenergy production that can be productive in agricultural lands that are not suitable for food production. There is growing interest in whether its associated microbiome may be adaptive in low- or no-input cultivation systems. However, the relative impact of plant genotype and soil factors on plant microbiome and biomass are a challenge to decouple. To address this, a common garden greenhouse experiment was carried out using six common switchgrass genotypes, which were each grown in four different marginal soils collected from long-term bioenergy research sites in Michigan and Wisconsin. We characterized the fungal and bacterial root communities with high-throughput amplicon sequencing of the ITS and 16S rDNA markers, and collected phenological plant traits during plant growth, as well as soil chemical traits. At harvest, we measured the total plant aerial dry biomass. Significant differences in richness and Shannon diversity across soils but not between plant genotypes were found. Generalized linear models showed an interaction between soil and genotype for fungal richness but not for bacterial richness. Community structure was also strongly shaped by soil origin and soil origin × plant genotype interactions. Overall, plant genotype effects were significant but low. Random Forest models indicate that important factors impacting switchgrass biomass included NO 3 – , Ca 2+ , PO 4 3– , and microbial biodiversity. We identified 54 fungal and 52 bacterial predictors of plant aerial biomass, which included several operational taxonomic units belonging to Glomeraceae and Rhizobiaceae, fungal and bacterial lineages that are involved in provisioning nutrients to plants.

plant biomass↗

Assembly of the Populus Microbiome Is Temporally Dynamic and Determined by Selective and Stochastic Factors

Recent work shows that the plant microbiome, particularly the initial assembly of this microbiome, influences plant health, survival, and fitness. Here, we characterize the initial assembly of the Populus microbiome across ten genotypes belonging to two poplar species in a common garden using 16S rRNA gene and ITS2 region amplicon sequencing of the leaf endosphere, leaf surface, root endosphere, and rhizosphere. We sampled these microbiomes three times throughout the first growing season and found that the composition of the microbiome changed dramatically over time across all plant-associated habitats and host genotypes. For archaea and bacteria, these changes were dominated by strong homogenizing selection (accounting for 29 to 62% of pairwise comparisons). However, fungal assembly was generally characterized by multiple ecological assembly processes (i.e., a mix of weak selective and dispersal processes). Interestingly, genotype, while a significant moderator of microbiome composition, generally explained less variation than sample date across plant-associated habitats. We defined a set of core genera that accounted for, on average, 36% of the microbiome. The relative abundance of this core community was consistent over time. Additionally, using source tracking modeling, we determined that new microbial taxa colonize from both aboveground and belowground sources, and combined with our ecological assembly null models, we found that both selective and dispersal processes explained the differences between exo- (i.e., leaf surface and rhizosphere) and endospheric microbiomes. Taken together, our results suggest that the initial assembly of the Populus microbiome is time-, genotype-, and habitat-dependent and is moderated by both selective and stochastic factors.

16S rRNA↗

A Simple, Cost-Effective, and Automation-Friendly Direct PCR Approach for Bacterial Community Analysis

Understanding bacterial interactions and assembly in complex microbial communities using 16S rRNA sequencing normally requires a large experimental load. However, the current DNA extraction methods, including cell disruption and genomic DNA purification, are normally biased, costly, time-consuming, labor-intensive, and not amenable to miniaturization by droplets or 1,536-well plates due to the significant DNA loss during the purification step for tiny-volume and low-cell-density samples.

16S rRNA sequencing↗