Functional analysis of the apple fruit microbiome based on shotgun metagenomic sequencing of conventional and organic orchard samples.

Fruits harbour abundant and diverse microbial communities that protect them from post-harvest pathogens. Identification of functional traits associated with a given microbiota can provide a better understanding of their potential influence. Here, we focused on the epiphytic microbiome of apple fruit. We suggest that shotgun metagenomic data can indicate specific functions carried out by different groups and provide information on their potential impact. Samples were collected from the surface of 'Golden Delicious' apples from four orchards that differ in their geographic location and management practice. Approximately 1 million metagenes were predicted based on a high-quality assembly. Functional profiling of the microbiome of fruits from orchards differing in their management practice revealed a functional shift in the microbiota. The organic orchard microbiome was enriched in pathways involved in plant defence activities; the conventional orchard microbiome was enriched in pathways related to the synthesis of antibiotics. The functional significance of the variations was explored using microbial network modelling algorithms to reveal the metabolic role of specific phylogenetic groups. The analysis identified several associations supported by other published studies. For example, the analysis revealed the nutritional dependencies of the Capnodiales group, including the Alternaria pathogen, on aromatic compounds.

A framework for the targeted recruitment of crop-beneficial soil taxa based on network analysis of metagenomics data.

BACKGROUND: The design of ecologically sustainable and plant-beneficial soil systems is a key goal in actively manipulating root-associated microbiomes. Community engineering efforts commonly seek to harness the potential of the indigenous microbiome through substrate-mediated recruitment of beneficial members. In most sustainable practices, microbial recruitment mechanisms rely on the application of complex organic mixtures where the resources/metabolites that act as direct stimulants of beneficial groups are not characterized. Outcomes of such indirect amendments are unpredictable regarding engineering the microbiome and achieving a plant-beneficial environment. RESULTS: This study applied network analysis of metagenomics data to explore amendment-derived transformations in the soil microbiome, which lead to the suppression of pathogens affecting apple root systems. Shotgun metagenomic analysis was conducted with data from 'sick' vs 'healthy/recovered' rhizosphere soil microbiomes. The data was then converted into community-level metabolic networks. Simulations examined the functional contribution of treatment-associated taxonomic groups and linked them with specific amendment-induced metabolites. This analysis enabled the selection of specific metabolites that were predicted to amplify or diminish the abundance of targeted microbes functional in the healthy soil system. Many of these predictions were corroborated by experimental evidence from the literature. The potential of two of these metabolites (dopamine and vitamin B12) to either stimulate or suppress targeted microbial groups was evaluated in a follow-up set of soil microcosm experiments. The results corroborated the stimulant's potential (but not the suppressor) to act as a modulator of plant beneficial bacteria, paving the way for future development of knowledge-based (rather than trial and error) metabolic-defined amendments. Our pipeline for generating predictions for the selective targeting of microbial groups based on processing assembled and annotated metagenomics data is available at https://github.com/ot483/NetCom2 . CONCLUSIONS: This research demonstrates how genomic-based algorithms can be used to formulate testable hypotheses for strategically engineering the rhizosphere microbiome by identifying specific compounds, which may act as selective modulators of microbial communities. Applying this framework to reduce unpredictable elements in amendment-based solutions promotes the development of ecologically-sound methods for re-establishing a functional microbiome in agro and other ecosystems. Video Abstract.

Comparative study of bacterial community dynamics in different soils following application of the herbicide atrazine.

Microbial communities in cultivated soils control the fate of pollutants associated with agricultural practice. The present study was designed to explore the response of bacterial communities to the application of the widely-used herbicide atrazine in three different crop fields that differ significantly in their physicochemical structure and nutritional content: the nutrient-rich (with relatively high carbon and nitrogen content) Newe Yaar (NY) and Ha-Ogen (HO) soils and the nutrient-poor, sandy Sde-Eliyahu (SE) soil. The 16 S rRNA gene amplicon sequencing revealed the nutrient poor HO soil differs in its response to atrazine in comparison to the two nutrient-rich soils both in the shortest persistence of atrazine and its effect on community structure and composition. Potential reported bacterial degraders of atrazine such as Pseudomonas, Clostridium and Bacillus were more abundant in contaminated sandy/poor soils (HO) whereas bacteria known for nitrogen cycling such as Azospirillum, Sinorhizobium, Nitrospira and Azohydromonas were significantly more abundant in the nutrient rich contaminated SE soils. No significant increase of potential indigenous degrader Arthrobacter was detected in SE and NY soils whereas a significant increase was recorded with HO soils. An overall shift in bacterial community composition following atrazine application was observed only in the nutrient poor soil. Understanding atrazine persistence and microbiome response to its application of in dependence with soil types serve the design of precision application strategies.

Short- and long-term effects of continuous compost amendment on soil microbiome community.

Organic amendment, and especially the use of composts, is a well-accepted sustainable agricultural practice. Compost increases soil carbon and microbial biomass, changes enzymatic activity, and enriches soil carbon and nitrogen stocks. However, relatively little is known about the immediate and long-term temporal dynamics of agricultural soil microbial communities following repeated compost applications. Our study was conducted at two field sites: Newe Ya'ar (NY, Mediterranean climate) and Gilat (G, semi-arid climate), both managed organically over 4 years under either conventional fertilization (0, zero compost) or three levels of compost amendment (20, 40 and 60 m3/ha or 2, 4, 6 L/m2). Microbial community dynamics in the soils was examined by high- and low-time-resolution analyses. Annual community composition in compost-amended soils was significantly affected by compost amendment levels in G (first, second and third years) and in NY (third year). Repeated sampling at high resolution (9-10 times over 1 year) showed that at both sites, compost application initially induced a strong shift in microbial communities, lasting for up to 1 month, followed by a milder response. Compost application significantly elevated alpha diversity at both sites, but differed in the compost-dose correlation effect. We demonstrate higher abundance of taxa putatively involved in organic decomposition and characterized compost-related indicator taxa and a compost-derived core microbiome at both sites. Overall, this study describes temporal changes in the ecology of soil microbiomes in response to compost vs. conventional fertilization.

Modeling-Guided Amendments Lead to Enhanced Biodegradation in Soil.

Extensive use of agrochemicals is emerging as a serious environmental issue coming at the cost of the pollution of soil and water resources. Bioremediation techniques such as biostimulation are promising strategies used to remove pollutants from agricultural soils by supporting the indigenous microbial degraders. Though considered cost-effective and eco-friendly, the success rate of these strategies typically varies, and consequently, they are rarely integrated into commercial agricultural practices. In the current study, we applied metabolic-based community-modeling approaches for promoting realistic in terra solutions by simulation-based prioritization of alternative supplements as potential biostimulants, considering a collection of indigenous bacteria. Efficacy of biostimulants as enhancers of the indigenous degrader Paenarthrobacter was ranked through simulation and validated in pot experiments. A two-dimensional simulation matrix predicting the effect of different biostimulants on additional potential indigenous degraders (Pseudomonas, Clostridium, and Geobacter) was crossed with experimental observations. The overall ability of the models to predict the compounds that act as taxa-selective stimulants indicates that computational algorithms can guide the manipulation of the soil microbiome in situ and provides an additional step toward the educated design of biostimulation strategies. IMPORTANCE Providing the food requirements of a growing population comes at the cost of intensive use of agrochemicals, including pesticides. Native microbial soil communities are considered key players in the degradation of such exogenous substances. Manipulating microbial activity toward an optimized outcome in efficient biodegradation processes conveys a promise of maintaining intensive yet sustainable agriculture. Efficient strategies for harnessing the native microbiome require the development of approaches for processing big genomic data. Here, we pursued metabolic modeling for promoting realistic in terra solutions by simulation-based prioritization of alternative supplements as potential biostimulants, considering a collection of indigenous bacteria. Our genomic-based predictions point at strategies for optimizing biodegradation by the native community. Developing a systematic, data-guided understanding of metabolite-driven targeted enhancement of selected microorganisms lays the foundation for the design of ecologically sound methods for optimizing microbiome functioning.

NetCom: A Network-Based Tool for Predicting Metabolic Activities of Microbial Communities Based on Interpretation of Metagenomics Data.

The study of microbial activity can be viewed as a triangle with three sides: environment (dominant resources in a specific habitat), community (species dictating a repertoire of metabolic conversions) and function (production and/or utilization of resources and compounds). Advances in metagenomics enable a high-resolution description of complex microbial communities in their natural environments and support a systematic study of environment-community-function associations. NetCom is a web-tool for predicting metabolic activities of microbial communities based on network-based interpretation of assembled and annotated metagenomics data. The algorithm takes as an input, lists of differentially abundant enzymatic reactions and generates the following outputs: (i) pathway associations of differently abundant enzymes; (ii) prediction of environmental resources that are unique to each treatment, and their pathway associations; (iii) prediction of compounds that are produced by the microbial community, and pathway association of compounds that are treatment-specific; (iv) network visualization of enzymes, environmental resources and produced compounds, that are treatment specific (2 and 3D). The tool is demonstrated on metagenomic data from rhizosphere and bulk soil samples. By predicting root-specific activities, we illustrate the relevance of our framework for forecasting the impact of soil amendments on the corresponding microbial communities. NetCom is available online.

An eco-systems biology approach for modeling tritrophic networks reveals the influence of dietary amino acids on symbiont dynamics of Bemisia tabaci.

Metabolic conversions allow organisms to produce essential metabolites from the available nutrients in an environment, frequently requiring metabolic exchanges among co-inhabiting organisms. Here, we applied genomic-based simulations for exploring tri-trophic interactions among the sap-feeding insect whitefly (Bemisia tabaci), its host-plants, and symbiotic bacteria. The simplicity of this ecosystem allows capturing the interacting organisms (based on genomic data) and the environmental content (based on metabolomics data). Simulations explored the metabolic capacities of insect-symbiont combinations under environments representing natural phloem. Predictions were correlated with experimental data on the dynamics of symbionts under different diets. Simulation outcomes depict a puzzle of three-layer origins (plant-insect-symbionts) for the source of essential metabolites across habitats and stratify interactions enabling the whitefly to feed on diverse hosts. In parallel to simulations, natural and artificial feeding experiments provide supporting evidence for an environment-based effect on symbiont dynamics. Based on simulations, a decrease in the relative abundance of a symbiont can be associated with a loss of fitness advantage due to an environmental excess in amino-acids whose production in a deprived environment used to depend on the symbiont. The study demonstrates that genomic-based predictions can bridge environment and community dynamics and guide the design of symbiont manipulation strategies.

Strategies for Enhancing in vitro Degradation of Linuron by Variovorax sp. Strain SRS 16 Under the Guidance of Metabolic Modeling.

Phenyl urea herbicides are being extensively used for weed control in both agricultural and non-agricultural applications. Linuron is one of the key herbicides in this family and is in wide use. Like other phenyl urea herbicides, it is known to have toxic effects as a result of its persistence in the environment. The natural removal of linuron from the environment is mainly carried through microbial biodegradation. Some microorganisms have been reported to mineralize linuron completely and utilize it as a carbon and nitrogen source. Variovorax sp. strain SRS 16 is one of the known efficient degraders with a recently sequenced genome. The genomic data provide an opportunity to use a genome-scale model for improving biodegradation. The aim of our study is the construction of a genome-scale metabolic model following automatic and manual protocols and its application for improving its metabolic potential through iterative simulations. Applying flux balance analysis (FBA), growth and degradation performances of SRS 16 in different media considering the influence of selected supplements (potential carbon and nitrogen sources) were simulated. Outcomes are predictions for the suitable media modification, allowing faster degradation of linuron by SRS 16. Seven metabolites were selected for in vitro validation of the predictions through laboratory experiments confirming the degradation-promoting effect of specific amino acids (glutamine and asparagine) on linuron degradation and SRS 16 growth. Overall, simulations are shown to be efficient in predicting the degradation potential of SRS 16 in the presence of specific supplements. The generated information contributes to the understanding of the biochemistry of linuron degradation and can be further utilized for the development of new cleanup solutions without any genetic manipulation.

NetMet: A Network-Based Tool for Predicting Metabolic Capacities of Microbial Species and their Interactions.

Metabolic conversions allow organisms to produce a set of essential metabolites from the available nutrients in an environment, frequently requiring metabolic exchanges among co-inhabiting organisms. Genomic-based metabolic simulations are being increasingly applied for exploring metabolic capacities, considering different environments and different combinations of microorganisms. NetMet is a web-based tool and a software package for predicting the metabolic performances of microorganisms and their corresponding combinations in user-defined environments. The algorithm takes, as input, lists of (i) species-specific enzymatic reactions (EC numbers), and (ii) relevant metabolic environments. The algorithm generates, as output, lists of (i) compounds that individual species can produce in each given environment, and (ii) compounds that are predicted to be produced through complementary interactions. The tool is demonstrated in two case studies. First, we compared the metabolic capacities of different haplotypes of the obligatory fruit and vegetable pathogen Candidatus Liberibacter solanacearum to those of their culturable taxonomic relative Liberibacter crescens. Second, we demonstrated the potential production of complementary metabolites by pairwise combinations of co-occurring endosymbionts of the plant phloem-feeding whitefly Bemisia tabaci. NetMet, a new web-based tool, is available at https://freilich-lab-tools.com/.

Modeling microbial communities from atrazine contaminated soils promotes the development of biostimulation solutions.

Microbial communities play a vital role in biogeochemical cycles, allowing the biodegradation of a wide range of pollutants. The composition of the community and the interactions between its members affect degradation rate and determine the identity of the final products. Here, we demonstrate the application of sequencing technologies and metabolic modeling approaches towards enhancing biodegradation of atrazine-a herbicide causing environmental pollution. Treatment of agriculture soil with atrazine is shown to induce significant changes in community structure and functional performances. Genome-scale metabolic models were constructed for Arthrobacter, the atrazine degrader, and four other non-atrazine degrading species whose relative abundance in soil was changed following exposure to the herbicide. By modeling community function we show that consortia including the direct degrader and non-degrader differentially abundant species perform better than Arthrobacter alone. Simulations predict that growth/degradation enhancement is derived by metabolic exchanges between community members. Based on simulations we designed endogenous consortia optimized for enhanced degradation whose performances were validated in vitro and biostimulation strategies that were tested in pot experiments. Overall, our analysis demonstrates that understanding community function in its wider context, beyond the single direct degrader perspective, promotes the design of biostimulation strategies.

Methodological aspects of microcalorimetry used to assess the dynamics of microbial activity during composting.

Isothermal microcalorimetry is a sensitive non-invasive analytical tool that can become useful in research on compost and other biosolids. The aim of the present study was to address several methodological aspects that are critical to the use of microcalorimetry to assess the dynamics of microbial activity in such systems. The results show that: (1) The calorimetric baseline is strongly influenced by the run temperature in the range relevant to composting systems (20-60 degrees C), and is also affected by addition of the water that is required to maintain or optimize microbial activity, presumably because some water evaporates through ampoule gaskets. (2) Amending mature compost with readily available substrates requires additional careful baseline treatment. (3) Sample heterogeneity can be successfully minimized by passing through a 2-mm sieve. Additional size separation can be useful to enable focusing on the more active fractions. (4) Oxygen depletion is a key feature in batch calorimetric analysis; for samples of highly active composts or manure, the total amount of heat released relative to the oxygen available in the ampoule may indicate the co-existence of anaerobic and aerobic metabolic pathways. Finally, practical recommendations for microcalorimetry analyses of pre-mature and mature composts are outlined.

Land spreading of olive mill wastewater: effects on soil microbial activity and potential phytotoxicity.

Extremely high organic load and the toxic nature of olive mill wastewater (OMW) prevent their direct discharge into domestic wastewater treatment systems. In addition to the various treatment schemes designed for such wastewater, controlled land spreading of untreated OMW has been suggested as an alternative mean of disposal. A field study was conducted between October 2004 and September 2005 to assess possible effects of OMW on soil microbial activity and potential phytotoxicity. The experiment was carried out in an organic orchard located on a Vertisol-type soil (Jezre'el Valley, Israel) and included two application levels of OMW (36 and 72m(3)ha(-1)). Total microbial counts, and to less extent the hydrolytic activity and soil respiration were increased following the high OMW application level. A bench-scale lab experiment showed that the rate of OMW mineralization was mainly dependent on the general status of soil activity and was not related to previous acclimatization of the soil microflora to OMW. Soil phytotoxicity (% germination and root elongation) was assessed in soil extracts of samples collected before and after each OMW application, using germinating cress (Lepidium sativum L.) seeds. We found direct short-term effect of OMW application on soil phytotoxicity. However, the soil was partly or completely recovered between successive applications. No further phytotoxicity was observed in treated soils as compared with control soil, 3 months after OMW application. Such short-term phytotoxicity was not in correlation with measured EC and total polyphenols in the soil extracts. Overall, the results of this study further support a safe controlled OMW spreading on lands that are not associated with sensitive aquifers.

High-nitrogen compost as a medium for organic container-grown crops.

Compost was tested as a medium for organic container-grown crops. Nitrogen (N) loss during composting of separated cow manure (SCM) was minimized using high C/N (wheat straw, WS; grape marc, GM) or a slightly acidic (orange peels, OP) additives. N conservation values in the resultant composts were 82%, 95% and 98% for GM-SCM, OP-SCM and WS-SCM, respectively. Physical characteristics of the composts were compatible with use as growing media. The nutritional contribution of the composts was assessed using cherry tomato (Lycopersicon esculantum Mill.) and by means of incubation experiments. Media were either unfertilized or fertilized with guano (sea-bird manure). Plant responses suggest that N availability is the main variable affecting growth. Unfertilized OP-SCM and WS-SCM supplied the N needed for at least 4 months of plant growth. Root-galling index (GI) of tomato roots and number of eggs of the nematode Meloidogyne javanica were reduced by the composts, with the highest reduction obtained by OP-SCM and WS-SCM, at 50% concentrations. These composts, but not peat, reduced the incidence of crown and root-rot disease in tomato as well as the population size of the causal pathogen, Fusarium oxysporum f. sp. radicis-lycopersici.