Soil warming duration and magnitude affect the dynamics of fine roots and rhizomes and associated C and N pools in subarctic grasslands.

BACKGROUND AND AIMS: The response of subarctic grassland's below-ground to soil warming is key to understanding this ecosystem's adaptation to future climate. Functionally different below-ground plant organs can respond differently to changes in soil temperature (Ts). We aimed to understand the below-ground adaptation mechanisms by analysing the dynamics and chemistry of fine roots and rhizomes in relation to plant community composition and soil chemistry, along with the duration and magnitude of soil warming. METHODS: We investigated the effects of the duration [medium-term warming (MTW; 11 years) and long-term warming (LTW; > 60 years)] and magnitude (0-8.4 °C) of soil warming on below-ground plant biomass (BPB), fine root biomass (FRB) and rhizome biomass (RHB) in geothermally warmed subarctic grasslands. We evaluated the changes in BPB, FRB and RHB and the corresponding carbon (C) and nitrogen (N) pools in the context of ambient, Ts < +2 °C and Ts > +2 °C scenarios. KEY RESULTS: BPB decreased exponentially in response to an increase in Ts under MTW, whereas FRB declined under both MTW and LTW. The proportion of rhizomes increased and the C-N ratio in rhizomes decreased under LTW. The C and N pools in BPB in highly warmed plots under MTW were 50 % less than in the ambient plots, whereas under LTW, C and N pools in warmed plots were similar to those in non-warmed plots. Approximately 78 % of the variation in FRB, RHB, and C and N concentration and pools in fine roots and rhizomes was explained by the duration and magnitude of soil warming, soil chemistry, plant community functional composition, and above-ground biomass. Plant's below-ground biomass, chemistry and pools were related to a shift in the grassland's plant community composition - the abundance of ferns increased and BPB decreased towards higher Ts under MTW, while the recovery of below-ground C and N pools under LTW was related to a higher plant diversity. CONCLUSION: Our results indicate that plant community-level adaptation of below ground to soil warming occurs over long periods. We provide insight into the potential adaptation phases of subarctic grasslands.

A critical review of the impacts of cover crops on nitrogen leaching, net greenhouse gas balance and crop productivity.

Cover crops play an increasingly important role in improving soil quality, reducing agricultural inputs and improving environmental sustainability. The main objectives of this critical global review and systematic analysis were to assess cover crop practices in the context of their impacts on nitrogen leaching, net greenhouse gas balances (NGHGB) and crop productivity. Only studies that investigated the impacts of cover crops and measured one or a combination of nitrogen leaching, soil organic carbon (SOC), nitrous oxide (N2 O), grain yield and nitrogen in grain of primary crop, and had a control treatment were included in the analysis. Long-term studies were uncommon, with most data coming from studies lasting 2-3 years. The literature search resulted in 106 studies carried out at 372 sites and covering different countries, climatic zones and management. Our analysis demonstrates that cover crops significantly (p < 0.001) decreased N leaching and significantly (p < 0.001) increased SOC sequestration without having significant (p > 0.05) effects on direct N2 O emissions. Cover crops could mitigate the NGHGB by 2.06 ± 2.10 Mg CO2 -eq ha-1  year-1 . One of the potential disadvantages of cover crops identified was the reduction in grain yield of the primary crop by ≈4%, compared to the control treatment. This drawback could be avoided by selecting mixed cover crops with a range of legumes and non-legumes, which increased the yield by ≈13%. These advantages of cover crops justify their widespread adoption. However, management practices in relation to cover crops will need to be adapted to specific soil, management and regional climatic conditions.

Impact of polyols on Oral microbiome of Estonian schoolchildren.

BACKGROUND: Oral microbiome has significant impact on both oral and general health. Polyols have been promoted as sugar substitutes in prevention of oral diseases. We aimed to reveal the effect of candies containing erythritol, xylitol or control (sorbitol) on salivary microbiome. METHODS: Ninety children (11.3 ± 0.6 years) consumed candies during 3 years. Microbial communities were profiled using Illumina HiSeq 2000 sequencing and real-time PCR. RESULTS: The dominant phyla in saliva were Firmicutes (39.1%), Proteobacteria (26.1%), Bacteroidetes (14.7%), Actinobacteria (12%) and Fusobacteria (6%). The microbiome of erythritol group significantly differed from that of the other groups. Both erythritol and xylitol reduced the number of observed bacterial phylotypes in comparison to the control group. The relative abundance of the genera Veillonella, Streptococcus and Fusobacterium were higher while that of Bergeyella lower after erythritol intervention when comparing with control. The lowest prevalence of caries-related mutans streptococci corresponded with the lowest clinical caries markers in the erythritol group. CONCLUSIONS: Daily consumption of erythritol, xylitol or control candies has a specific influence on the salivary microbiome composition in schoolchildren. Erythritol is associated with the lowest prevalence of caries-related mutans streptococci and the lowest levels of clinical caries experience. TRIAL REGISTRATION: ClinicalTrials.gov Identifier NCT01062633.

Adaptive root foraging strategies along a boreal-temperate forest gradient.

The tree root-mycorhizosphere plays a key role in resource uptake, but also in the adaptation of forests to changing environments. The adaptive foraging mechanisms of ectomycorrhizal (EcM) and fine roots of Picea abies, Pinus sylvestris and Betula pendula were evaluated along a gradient from temperate to subarctic boreal forest (38 sites between latitudes 48°N and 69°N) in Europe. Variables describing tree resource uptake structures and processes (absorptive fine root biomass and morphology, nitrogen (N) concentration in absorptive roots, extramatrical mycelium (EMM) biomass, community structure of root-associated EcM fungi, soil and rhizosphere bacteria) were used to analyse relationships between root system functional traits and climate, soil and stand characteristics. Absorptive fine root biomass per stand basal area increased significantly from temperate to boreal forests, coinciding with longer and thinner root tips with higher tissue density, smaller EMM biomass per root length and a shift in soil microbial community structure. The soil carbon (C) : N ratio was found to explain most of the variability in absorptive fine root and EMM biomass, root tissue density, N concentration and rhizosphere bacterial community structure. We suggest a concept of absorptive fine root foraging strategies involving both qualitative and quantitative changes in the root-mycorrhiza-bacteria continuum along climate and soil C : N gradients.

Seminal microbiome in men with and without prostatitis.

OBJECTIVES: To profile the seminal microbiome applying next generation sequencing. METHODS: Semen samples of 67 men were involved in the study (21 men with and 46 men without prostatitis). Seminal microbiomes were profiled applying the method that uses combinatorial sequence tags attached to polymerase chain reaction primers that amplify the ribosomal ribonucleic acid V6 region. Amplified polymerase chain reaction products were sequenced using an Illumina paired-end protocol on HiSeq2000 platform. RESULTS: The most abundant phylum in semen was Firmicutes, comprising nearly half of the sequences found (median 41.7%, quartiles 28.5-47.2%) followed by Bacteroidetes, Proteobacteria and Actinobacteria. The counts of lactobacilli were higher in healthy men than prostatitis patients (27% [20.2-34.6%] vs 20.2% [4.9-25.0%]; P = 0.05), especially for Lactobacillus iners. Proteobacteria comprised higher proportions in prostatitis patients than healthy men. The species richness was higher in prostatitis patients than healthy men (inverted Simpson index 13.5 ± 5.8 vs 10.3 ± 4.0). CONCLUSIONS: The semen of chronic prostatitis patients contains fewer health-supporting lactobacilli, and has higher species diversity than that of healthy men. Firmicutes (especially lactobacilli), Bacteroidetes, Proteobacteria and Actinobacteria comprise the highest proportion of seminal microbiome.

Isotopologue ratios of N2O and N2 measurements underpin the importance of denitrification in differently N-loaded riparian alder forests.

Known as biogeochemical hotspots in landscapes, riparian buffer zones exhibit considerable potential concerning mitigation of groundwater contaminants such as nitrate, but may in return enhance the risk for indirect N2O emission. Here we aim to assess and to compare two riparian gray alder forests in terms of gaseous N2O and N2 fluxes and dissolved N2O, N2, and NO3(-) in the near-surface groundwater. We further determine for the first time isotopologue ratios of N2O dissolved in the riparian groundwater in order to support our assumption that it mainly originated from denitrification. The study sites, both situated in Estonia, northeastern Europe, receive contrasting N loads from adjacent uphill arable land. Whereas N2O emissions were rather small at both sites, average gaseous N2-to-N2O ratios inferred from closed-chamber measurements and He-O laboratory incubations were almost four times smaller for the heavily loaded site. In contrast, groundwater parameters were less variable among sites and between landscape positions. Campaign-based average (15)N site preferences of N2O (SP) in riparian groundwater ranged between 11 and 44 ‰. Besides the strong prevalence of N2 emission over N2O fluxes and the correlation pattern between isotopologue and water quality data, this comparatively large range highlights the importance of denitrification and N2O reduction in both riparian gray alder stands.

Characterization of the bacterioplankton community and its antibiotic resistance genes in the Baltic Sea.

The residues from human environments often contain antibiotics and antibiotic resistance genes (ARGs) that can contaminate natural environments; the clearest consequence of that is the selection of antibiotic-resistant bacteria. The Baltic Sea is the second largest isolated brackish water reservoir on Earth, serving as a drainage area for people in 14 countries, which differ from one another in antibiotic use and sewage treatment policies. The aim of this study was to characterize the bacterioplankton structure and quantify ARGs (tetA, tetB, tetM, ermB, sul1, blaSHV, and ampC) within the bacterioplankton community of the Baltic Sea. Quantitative polymerase chain reaction was applied to quantify ARGs from four different sampling sites of the Baltic Sea over 2 years, and the bacterial communities were profiled sequencing the V6 region of the 16S rRNA gene on Illumina HiSeq2000. The results revealed that all the resistance genes targeted in the study were detectable from the Baltic Sea bacterioplankton. The percentage of tetA, tetB, tetM, ermB, and sul1 genes in the sea bacterial community varied between 0.0077% and 0.1089%, 0.0003% and 0.0019%, 0.0001% and 0.0105%, 0% and 0.0136%, and 0.0001% and 0.0438%, respectively. The most numerous ARG detected was the tetA gene and this gene also had the highest proportion in the whole microbial community. A strong association between bacterioplankton ARGs' abundance data and community phylogenetic composition was found, implying that the abundance of most of the studied ARGs in the Baltic Sea is determined by fluctuations in its bacterial community structure.

The effects of woodchip- and straw-derived biochars on the persistence of the herbicide 4-chloro-2-methylphenoxyacetic acid (MCPA) in soils.

Sorption and degradation are the primary processes controlling the efficacy and runoff contamination risk of agrochemicals. This study assessed the influence of two biochars, made from woodchips and straw at a pyrolysis temperature of 725°C and applied to a loamy sand and a sandy soil in the concentration of 5.3 g 100 g(-1) sandy soil and 4.1 g 100 g(-1) loamy sand soil, or 53 t ha(-1) for both soil types, on degradation of the herbicide 4-chloro-2-methylphenoxyacetic acid (MCPA). Soils were spiked with 50 mg MCPA kg(-1) soil. In the sandy soil, significantly more MCPA remained after 100 days if amended with straw-derived biochar in comparison to wood-derived biochar. Both biochars types significantly increased urease activity (p<0.05) after 37 days in the loamy sand soil, but these differences disappeared after 100 days. A root and shoot elongation test demonstrated that the soils containing straw-derived biochar and spiked with MCPA, showed the highest phytotoxicity. Both biochars were found to retard MCPA degradation in loamy sand and sandy soils. This effect could not be explained only by sorption processes due to comparatively low developed micro/mesoporous structure of both biochars shown by BET surface analysis. However, an enhanced MCPA persistence and soil toxicity in sandy soil amended with straw biochar was observed and further studies are needed to reveal the responsible mechanisms.

Effect of pine bark on the biotransformation of trinitrotoluene and on the bacterial community structure in a batch experiment.

Pine bark, a low-cost industrial residue, has been suggested as a promising substitute for granular activated carbon in the on-site treatment of water contaminated with 2,4,6-trinitrotoluene (TNT). However, the complex organic structure and indigenous microbial community of pine bark have thus far not been thoroughly described in the context of TNT-contaminated water treatment. This two-week batch study examined the removal efficiency ofTNT from water by (1) adsorption on pine bark and (2) simultaneous adsorption on pine bark and biotransformation by specialized TNT-biotransforming microbial inocula. The bacterial community composition of experimental batches, inocula and pine bark, was profiled by Illumina sequencing of the V6 region of the 16S rRNA gene. The results revealed that the inocula and experimental batches were dominated by phylotypes belonging to the Enterobacteriaceae family and that the tested inocula had good potential for TNT biotransformation. The type of applied inocula had the most profound effect on the TNT-transforming bacterial community structure in the experimental batches. The indigenous microbial community of pine bark harboured phylotypes that also have a potential to degrade TNT. Altogether, the combination of a specialized inoculum and pine bark proved to be the most efficient treatment option for TNT-contaminated water.

Transformation of phenylarsenic chemical warfare agents and their effect on bacterial communities in Baltic Sea sediment.

During the World Wars large quantities of phenylarsenic chemical warfare agents (CWAs) were dumped in the Baltic Sea. Many transformation products of these chemicals have been identified, but the pathways that produce the found chemicals has not been investigated. Here we studied the biotic and abiotic transformation of phenylarsenic CWAs under oxic and anoxic conditions and investigated how the sediment bacterial communities are affected by CWA exposure. By chemical analysis we were able to identify seventeen CWA-related phenylarsenicals, four of which (methylphenylarsinic acid (MPAA), phenylthioarsinic acid (PTAA), phenyldithioarsinic acid (PDTAA) and diphenyldithioarsinic acid (DPDTAA)) have not been reported for marine sediments before. For the first time PTAA was verified from environmental samples. We also observed equilibrium reactions between the found transformation products, which may explain the occurrence of the chemicals. 16S rRNA-analysis showed that bacterial communities in sediments are affected by exposure to phenylarsenic CWAs. We observed increases in the amounts of arsenic-resistant and sulphur-metabolising bacteria. Different transformation products were found in biotic and abiotic samples, which suggests that bacteria participate in the transformation of phenylarsenic CWAs. We propose that methylated phenylarsenicals are produced in microbial metabolism and that chemical reactions with microbially produced sulphur species form sulphur-containing transformation products.

Oil Biodegradation and Bioremediation in Cold Marine Environment.

Petroleum hydrocarbons pose a substantial threat to marine ecosystems [...].

Effects of Dispersant on the Petroleum Hydrocarbon Biodegradation and Microbial Communities in Seawater from the Baltic Sea and Norwegian Sea.

Dispersants have been used in several oil spill accidents, but little information is available on their effectiveness in Baltic Sea conditions with low salinity and cold seawater. This study investigated the effects of dispersant use on petroleum hydrocarbon biodegradation rates and bacterial community structures. Microcosm experiments were conducted at 5 °C for 12 days with North Sea crude oil and dispersant Finasol 51 with open sea Gulf of Bothnia and coastal Gulf of Finland and Norwegian Sea seawater. Petroleum hydrocarbon concentrations were analysed with GC-FID. Bacterial community structures were studied using 16S rDNA gene amplicon sequencing, and the abundance of genes involved in hydrocarbon degradation with quantitative PCR. The highest oil degradation gene abundances and oil removal were observed in microcosms with coastal seawater from the Gulf of Bothnia and Gulf of Finland, respectively, and the lowest in the seawater from the Norwegian Sea. Dispersant usage caused apparent effects on bacterial communities in all treatments; however, the dispersant's effect on the biodegradation rate was unclear due to uncertainties with chemical analysis and variation in oil concentrations used in the experiments.

The effect of synthetic silver nanoparticles on the antibiotic resistome and the removal efficiency of antibiotic resistance genes in a hybrid filter system treating municipal wastewater.

Engineered nanoparticles, including silver nanoparticles (AgNPs), are released into the environment mainly through wastewater treatment systems. Knowledge of the impact of AgNPs on the abundance and removal efficiency of antibiotic resistance genes (ARGs) in wastewater treatment facilities, including constructed wetlands (CWs), is essential in the context of public health. This study evaluated the effect of increased (100-fold) collargol (protein-coated AgNPs) and ionic Ag+ in municipal wastewater on the structure, abundance, and removal efficiency of the antibiotic resistome, integron-integrase genes, and pathogens in a hybrid CW using quantitative PCR and metagenomic approaches. The abundance of ARGs in wastewater and the removal efficiency of ARGs in the hybrid system were significantly affected by higher Ag concentrations, especially with collargol treatment, resulting in an elevated ARG discharge of system effluent into the environment. The accumulated Ag in the filters had a more profound effect on the absolute and relative abundance of ARGs in the treated water than the Ag content in the water. This study recorded significantly enhanced relative abundance values for tetracycline (tetA, tetC, tetQ), sulfonamide (sul1, sul2), and aminoglycoside (aadA) resistance genes, which are frequently found on mobile genetic elements in collargol- and, to a lesser extent, AgNO3-treated subsystems. Elevated plasmid and integron-integrase gene levels, especially intI1, in response to collargol presence indicated the substantial role of AgNPs in promoting horizontal gene transfer in the treatment system. The pathogenic segment of the prokaryotic community was similar to a typical sewage community, and strong correlations between pathogen and ARG proportions were recorded in vertical subsurface flow filters. Furthermore, the proportion of Salmonella enterica was positively related to the Ag content in these filter effluents. The effect of AgNPs on the nature and characteristics of prominent resistance genes carried by mobile genetic elements in CWs requires further investigation.

A toolbox of machine learning software to support microbiome analysis.

The human microbiome has become an area of intense research due to its potential impact on human health. However, the analysis and interpretation of this data have proven to be challenging due to its complexity and high dimensionality. Machine learning (ML) algorithms can process vast amounts of data to uncover informative patterns and relationships within the data, even with limited prior knowledge. Therefore, there has been a rapid growth in the development of software specifically designed for the analysis and interpretation of microbiome data using ML techniques. These software incorporate a wide range of ML algorithms for clustering, classification, regression, or feature selection, to identify microbial patterns and relationships within the data and generate predictive models. This rapid development with a constant need for new developments and integration of new features require efforts into compile, catalog and classify these tools to create infrastructures and services with easy, transparent, and trustable standards. Here we review the state-of-the-art for ML tools applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on ML based software and framework resources currently available for the analysis of microbiome data in humans. The aim is to support microbiologists and biomedical scientists to go deeper into specialized resources that integrate ML techniques and facilitate future benchmarking to create standards for the analysis of microbiome data. The software resources are organized based on the type of analysis they were developed for and the ML techniques they implement. A description of each software with examples of usage is provided including comments about pitfalls and lacks in the usage of software based on ML methods in relation to microbiome data that need to be considered by developers and users. This review represents an extensive compilation to date, offering valuable insights and guidance for researchers interested in leveraging ML approaches for microbiome analysis.

Advancing microbiome research with machine learning: key findings from the ML4Microbiome COST action.

The rapid development of machine learning (ML) techniques has opened up the data-dense field of microbiome research for novel therapeutic, diagnostic, and prognostic applications targeting a wide range of disorders, which could substantially improve healthcare practices in the era of precision medicine. However, several challenges must be addressed to exploit the benefits of ML in this field fully. In particular, there is a need to establish "gold standard" protocols for conducting ML analysis experiments and improve interactions between microbiome researchers and ML experts. The Machine Learning Techniques in Human Microbiome Studies (ML4Microbiome) COST Action CA18131 is a European network established in 2019 to promote collaboration between discovery-oriented microbiome researchers and data-driven ML experts to optimize and standardize ML approaches for microbiome analysis. This perspective paper presents the key achievements of ML4Microbiome, which include identifying predictive and discriminatory 'omics' features, improving repeatability and comparability, developing automation procedures, and defining priority areas for the novel development of ML methods targeting the microbiome. The insights gained from ML4Microbiome will help to maximize the potential of ML in microbiome research and pave the way for new and improved healthcare practices.

Assessment of Hydrocarbon Degradation Potential in Microbial Communities in Arctic Sea Ice.

The anthropogenic release of oil hydrocarbons into the cold marine environment is an increasing concern due to the elevated usage of sea routes and the exploration of new oil drilling sites in Arctic areas. The aim of this study was to evaluate prokaryotic community structures and the genetic potential of hydrocarbon degradation in the metagenomes of seawater, sea ice, and crude oil encapsulating the sea ice of the Norwegian fjord, Ofotfjorden. Although the results indicated substantial differences between the structure of prokaryotic communities in seawater and sea ice, the crude oil encapsulating sea ice (SIO) showed increased abundances of many genera-containing hydrocarbon-degrading organisms, including Bermanella, Colwellia, and Glaciecola. Although the metagenome of seawater was rich in a variety of hydrocarbon degradation-related functional genes (HDGs) associated with the metabolism of n-alkanes, and mono- and polyaromatic hydrocarbons, most of the normalized gene counts were highest in the clean sea ice metagenome, whereas in SIO, these counts were the lowest. The long-chain alkane degradation gene almA was detected from all the studied metagenomes and its counts exceeded ladA and alkB counts in both sea ice metagenomes. In addition, almA was related to the most diverse group of prokaryotic genera. Almost all 18 good- and high-quality metagenome-assembled genomes (MAGs) had diverse HDGs profiles. The MAGs recovered from the SIO metagenome belonged to the abundant taxa, such as Glaciecola, Bermanella, and Rhodobacteracea, in this environment. The genera associated with HDGs were often previously known as hydrocarbon-degrading genera. However, a substantial number of new associations, either between already known hydrocarbon-degrading genera and new HDGs or between genera not known to contain hydrocarbon degraders and multiple HDGs, were found. The superimposition of the results of comparing HDG associations with taxonomy, the HDG profiles of MAGs, and the full genomes of organisms in the KEGG database suggest that the found relationships need further investigation and verification.

Application of data integration for rice bacterial strain selection by combining their osmotic stress response and plant growth-promoting traits.

Agricultural application of plant-beneficial bacteria to improve crop yield and alleviate the stress caused by environmental conditions, pests, and pathogens is gaining popularity. However, before using these bacterial strains in plant experiments, their environmental stress responses and plant health improvement potential should be examined. In this study, we explored the applicability of three unsupervised machine learning-based data integration methods, including principal component analysis (PCA) of concatenated data, multiple co-inertia analysis (MCIA), and multiple kernel learning (MKL), to select osmotic stress-tolerant plant growth-promoting (PGP) bacterial strains isolated from the rice phyllosphere. The studied datasets consisted of direct and indirect PGP activity measurements and osmotic stress responses of eight bacterial strains previously isolated from the phyllosphere of drought-tolerant rice cultivar. The production of phytohormones, such as indole-acetic acid (IAA), gibberellic acid (GA), abscisic acid (ABA), and cytokinin, were used as direct PGP traits, whereas the production of hydrogen cyanide and siderophore and antagonistic activity against the foliar pathogens Pyricularia oryzae and Helminthosporium oryzae were evaluated as measures of indirect PGP activity. The strains were subjected to a range of osmotic stress levels by adding PEG 6000 (0, 11, 21, and 32.6%) to their growth medium. The results of the osmotic stress response experiments showed that all bacterial strains accumulated endogenous proline and glycine betaine (GB) and exhibited an increase in growth, when osmotic stress levels were increased to a specific degree, while the production of IAA and GA considerably decreased. The three applied data integration methods did not provide a similar grouping of the strains. Especially deviant was the ordination of microbial strains based on the PCA of concatenated data. However, all three data integration methods indicated that the strains Bacillus altitudinis PB46 and B. megaterium PB50 shared high similarity in PGP traits and osmotic stress response. Overall, our results indicate that data integration methods complement the single-table data analysis approach and improve the selection process for PGP microbial strains.

Impact of synthetic silver nanoparticles on the biofilm microbial communities and wastewater treatment efficiency in experimental hybrid filter system treating municipal wastewater.

Silver nanoparticles (AgNPs) threaten human and ecosystem health, and are among the most widely used engineered nanomaterials that reach wastewater during production, usage, and disposal phases. This study evaluated the effect of a 100-fold increase in collargol (protein-coated AgNP) and Ag+ ions concentrations in municipal wastewater on the microbial community composition of the filter material biofilms (FMB) and the purification efficiency of the hybrid treatment system consisting of vertical (VF) and horizontal (HF) subsurface flow filters. We found that increased amounts of collargol and AgNO3 in wastewater had a modest effect on the prokaryotic community composition in FMB and did not significantly affect the performance of the studied system. Regardless of how Ag was introduced, 99.9% of it was removed by the system. AgNPs and AgNO3 concentrations did not significantly affect the purification efficiency of the system. AgNO3 induced a higher increase in the genetic potential of certain Ag resistance mechanisms in VFs than collargol; however, the increase in Ag resistance potential was similar for both substances in HF. Hence, the microbial community composition in biofilms of vertical and horizontal flow filters is largely resistant, resilient, or functionally redundant in response to AgNPs addition in the form of collargol.

Microbial Community Dynamics during Biodegradation of Crude Oil and Its Response to Biostimulation in Svalbard Seawater at Low Temperature.

The development of oil exploration activities and an increase in shipping in Arctic areas have increased the risk of oil spills in this cold marine environment. The objective of this experimental study was to assess the effect of biostimulation on microbial community abundance, structure, dynamics, and metabolic potential for oil hydrocarbon degradation in oil-contaminated Arctic seawater. The combination of amplicon-based and shotgun sequencing, together with the integration of genome-resolved metagenomics and omics data, was applied to assess microbial community structure and metabolic properties in naphthenic crude oil-amended microcosms. The comparison of estimates for oil-degrading microbial taxa obtained with different sequencing and taxonomic assignment methods showed substantial discrepancies between applied methods. Consequently, the data acquired with different methods was integrated for the analysis of microbial community structure, and amended with quantitative PCR, producing a more objective description of microbial community dynamics and evaluation of the effect of biostimulation on particular microbial taxa. Implementing biostimulation of the seawater microbial community with the addition of nutrients resulted in substantially elevated prokaryotic community abundance (103-fold), a distinctly different bacterial community structure from that in the initial seawater, 1.3-fold elevation in the normalized abundance of hydrocarbon degradation genes, and 12% enhancement of crude oil biodegradation. The bacterial communities in biostimulated microcosms after four months of incubation were dominated by Gammaproteobacterial genera Pseudomonas, Marinomonas, and Oleispira, which were succeeded by Cycloclasticus and Paraperlucidibaca after eight months of incubation. The majority of 195 compiled good-quality metagenome-assembled genomes (MAGs) exhibited diverse hydrocarbon degradation gene profiles. The results reveal that biostimulation with nutrients promotes naphthenic oil degradation in Arctic seawater, but this strategy alone might not be sufficient to effectively achieve bioremediation goals within a reasonable timeframe.

The Foliar Application of Rice Phyllosphere Bacteria induces Drought-Stress Tolerance in Oryza sativa (L.).

This study assessed the potential of Bacillus endophyticus PB3, Bacillus altitudinis PB46, and Bacillus megaterium PB50 to induce drought tolerance in a susceptible rice cultivar. The leaves of the potted rice plants subjected to physical drought stress for 10 days during the flowering stage were inoculated with single-strain suspensions. Control pots of irrigated and drought-stressed plants were included in the experiment for comparison. In all treatments, the plant stress-related physiochemical and biochemical changes were examined and the expression of six stress-responsive genes in rice leaves was evaluated. The colonization potential on the surface of the rice leaves and stomata of the most successful strain in terms of induced tolerance was confirmed in the gnotobiotic experiment. The plants sprayed with B. megaterium PB50 showed an elevated stress tolerance based on their higher relative water content and increased contents of total sugars, proteins, proline, phenolics, potassium, calcium, abscisic acid, and indole acetic acid, as well as a high expression of stress-related genes (LEA, RAB16B, HSP70, SNAC1, and bZIP23). Moreover, this strain improved yield parameters compared to other treatments and also confirmed its leaf surface colonization. Overall, this study indicates that the foliar application of B. megaterium PB50 can induce tolerance to drought stress in rice.