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.

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.

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.

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.

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.

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.

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.

Conventional agriculture and not drought alters relationships between soil biota and functions.

Soil biodiversity constitutes the biological pillars of ecosystem services provided by soils worldwide. Soil life is threatened by intense agricultural management and shifts in climatic conditions as two important global change drivers which are not often jointly studied under field conditions. We addressed the effects of experimental short-term drought over the wheat growing season on soil organisms and ecosystem functions under organic and conventional farming in a Swiss long term trial. Our results suggest that activity and community metrics are suitable indicators for drought stress while microbial communities primarily responded to agricultural practices. Importantly, we found a significant loss of multiple pairwise positive and negative relationships between soil biota and process-related variables in response to conventional farming, but not in response to experimental drought. These results suggest a considerable weakening of the contribution of soil biota to ecosystem functions under long-term conventional agriculture. Independent of the farming system, experimental and seasonal (ambient) drought conditions directly affected soil biota and activity. A higher soil water content during early and intermediate stages of the growing season and a high number of significant relationships between soil biota to ecosystem functions suggest that organic farming provides a buffer against drought effects.

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.

Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment.

The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach.

Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions.

The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 "ML4Microbiome" that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies.

Soil Bacterial and Archaeal Communities and Their Potential to Perform N-Cycling Processes in Soils of Boreal Forests Growing on Well-Drained Peat.

Peatlands are unique wetland ecosystems that cover approximately 3% of the world's land area and are mostly located in boreal and temperate regions. Around 15 Mha of these peatlands have been drained for forestry during the last century. This study investigated soil archaeal and bacterial community structure and abundance, as well as the abundance of marker genes of nitrogen transformation processes (nitrogen fixation, nitrification, denitrification, and dissimilatory nitrate reduction to ammonia) across distance gradients from drainage ditches in nine full-drained, middle-aged peatland forests dominated by Scots pine, Norway spruce, or Downy birch. The dominating tree species had a strong effect on the chemical properties (pH, N and C/N status) of initially similar Histosols and affected the bacterial and archaeal community structure and abundance of microbial groups involved in the soil nitrogen cycle. The pine forests were distinguished by having the lowest fine root biomass of trees, pH, and N content and the highest potential for N fixation. The distance from drainage ditches affected the spatial distribution of bacterial and archaeal communities (especially N-fixers, nitrifiers, and denitrifiers possessing nosZ clade II), but this effect was often dependent on the conditions created by the dominance of certain tree species. The composition of the nitrifying microbial community was dependent on the soil pH, and comammox bacteria contributed significantly to nitrate formation in the birch and spruce soils where the pH was higher than 4.6. The highest N2O emission was recorded from soils with higher bacterial and archaeal phylogenetic diversity such as birch forest soils. This study demonstrates that the long-term growth of forests dominated by birch, pine, and spruce on initially similar organic soil has resulted in tree-species-specific changes in the soil properties and the development of forest-type-specific soil prokaryotic communities with characteristic functional properties and relationships within microbial communities.

Effects of simulated drought on biological soil quality, microbial diversity and yields under long-term conventional and organic agriculture.

Drought and agricultural management influence soil microorganisms with unknown consequences for the functioning of agroecosystems. We simulated drought periods in organic (biodynamic) and conventional wheat fields and monitored effects on soil water content, microorganisms and crops. Above the wilting point, water content and microbial respiration were higher under biodynamic than conventional farming. Highest bacterial and fungal abundances were found in biodynamically managed soils, and distinct microbial communities characterised the farming systems. Most biological soil quality parameters and crop yields were only marginally affected by the experimental drought, except for arbuscular mycorrhizal fungi (AMF), which increased in abundance under the experimental drought in both farming systems. AMF were further strongly promoted by biodynamic farming resulting in almost three times higher AMF abundance under experimental drought in the biodynamic compared with the conventional farming system. Our data suggest an improved water storage capacity under biodynamic farming and confirms positive effects of biodynamic farming on biological soil quality. The interactive effects of the farming system and drought may further be investigated under more substantial droughts. Given the importance of AMF for the plant's water supply, more in-depth studies on AMF may help to clarify their role for yields under conditions predicted by future climate scenarios.

Antibiotic resistome and microbial community structure during anaerobic co-digestion of food waste, paper and cardboard.

Solid organic waste is a significant source of antibiotic resistance genes (ARGs) and effective treatment strategies are urgently required to limit the spread of antimicrobial resistance. Here, we studied ARG diversity and abundance as well as the relationship between antibiotic resistome and microbial community structure within a lab-scale solid-state anaerobic digester treating a mixture of food waste, paper and cardboard. A total of 10 samples from digester feed and digestion products were collected for microbial community analysis including small subunit rRNA gene sequencing, total community metagenome sequencing and high-throughput quantitative PCR. We observed a significant shift in microbial community composition and a reduction in ARG diversity and abundance after 6 weeks of digestion. ARGs were identified in all samples with multidrug resistance being the most abundant ARG type. Thirty-two per cent of ARGs detected in digester feed were located on plasmids indicating potential for horizontal gene transfer. Using metagenomic assembly and binning, we detected potential bacterial hosts of ARGs in digester feed, which included Erwinia, Bifidobacteriaceae, Lactococcus lactis and Lactobacillus. Our results indicate that the process of sequential solid-state anaerobic digestion of food waste, paper and cardboard tested herein provides a significant reduction in the relative abundance of ARGs per 16S rRNA gene.

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.