1. Modeling based design of soil amendments that lead to enhanced biodegradation of soil pollutants including BTEX and herbicides

In a pioneering project, we demonstrated the usefulness of metabolic modeling approaches for the design of microbial consortium with soil-pollutant (the herbicide atrazine) degrading activity. To this end, we used sequence-based genomic information on the dynamics in soil community from atrazine treated fields in order to construct a corresponding imputed in silico community and used simulations for exploring the functional significance of community dynamics and for predicting possible bioremediation strategies. Based on simulations, we established a novel biodegradation consortium composed of endogenous soil bacteria and predicted the mechanisms – specific metabolic exchanges between species, directly and indirectly, involved in the degradation - behind the improvement in degradation performance. Predictions were experimentally validated and the consortium outperformed the known primary atrazine degrader Arthrobacter.

The study demonstrates how a combination of genomic and metabolic modeling approaches can solve a challenging problem that cannot be solved with a single technique alone and that bioremediation strategies should aim to be designed based on knowledge of: (1) the microorganisms that are present in the contaminated environments; (2) their metabolic capabilities; and (3) how they respond to changes in environmental conditions.

Nowadays, we are testing the application of the approach towards several practical solutions including:

  1. The development of commercial clean up solutions of agricultural soil (In collaboration with Prof. Hanan Eizenberg and Prof. Zeev Ronen)

  2. Enhancing the biodegradation of BTEX contaminants (In collaboration with Dr. Keren Golub Yanuka, Prof Isam Sabbah, Prof. Dror Avisar, and Dr. Katie Baransi Karkaby). See more about this project on Jenny Yusim's personal page.  

Metabolic modeling is led by Dr. Raphy Zarecki.





Summary of the work in Xu et al, ISME J 2019 ( Identification of potential biodegradation consortium members by comparing the microbiota in control and polluted soil. In simulations with a metabolic model as well as in in vitro experiments, the Arthrobacter–Halobacillus consortium degraded the pollutant atrazine more efficiently than other consortia or than Arthrobacter alone. The model also predicted the boost in degradation efficiencies due to cross-feeding between Arthrobacter and Halobacillus, which was confirmed in vitro (Figure is taken from Faust, Trends in Biotechnology 2018, a spotlight on our work,

2. Management of the soil microbiome in cropping agro-ecosystems

Many soil-borne diseases have efficient and sustainable, amendment-based solutions. The success of such a substrate-based (amendment-based) treatment to stimulate a microbiome-mediated disease control strategy is determined by the introduction of accessible metabolites that are beneficial to organisms functional in disease control or deleterious to organisms contributing to disease progression. Metagenomic surveys allow exploring the significance of shifts in community structure by comparing the functional potential of different samples. We apply network approaches for finding environmental friendly solutions for soil-borne diseases, based on the analyses of metagenomics data from healthy vs.  Symptomatic apple orchards following effective and non-effective soil amendment treatments. Outcome of the analysis was the formulation of predictions for specific metabolites that when added to the soil support specific microbial groups. Validations are now days being tested. Such integration of metagenomics data will hopefully lay foundations for the educated design of sustainable solutions for suppressing soilborne disease symptoms through substrate-mediated recruitment of disease-suppressive microbiomes in cropping systems. The project is a collaboration with Prof. Mark Mazzola and Tracey Somera. See more about this project on the personal pages of Maria Berihu and Alon Ginatt.




















Use of soil amendment treatments for the suppression of soil amendment treatment in apple orchards (Mark Mazzola, USDA-ARS, Figure taken from Mazolla & Freilich Phytopathology 2017, Relative growth performance of apple trees on a soil-borne disease infected site when cultivated in (left to right) fumigated soil (chemical treatment), substrate-based amended soil or non-treated infected orchard soil. Soil amendment produced a soil microbiological environment that was resistant to re-infestation by apple root pathogens. In contrast, fumigated soils were rapidly recolonized by pathogens. Below: Principal Coordinates Analysis (PCoA) plots of Bray-Curtis dissimilarities in the functional groups in root bacterial communities based on count tables derived from the metagenome analysis. Analysis indicates that the symptomatic change is associated with a change of function in the microbial communities. Network approaches are applied for deciphering this change (From Berihu et al. Under revision). 

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Other projects that focus on the health of the soil system in agroecosystems include a project concerning  Adapting Soil Biosolarization for Control of pathogens, in collaboration with James Stapleton and Christopher Simmons, and a project concerning the harnessing the soil food web for the biological control of root-knot nematodes, led by Eric Pelevsky. See more about this project on Hod Castel's page.

3. Developing ('omic) data-guided strategies for the reducing post-harvest pathology.

Genomic technologies have provided new insights on the process of disease progression leading to the emergence of a holistic view. The newly emerging 'pathobiome' concept suggests a conceptual shift from the one pathogen - one disease concept of Koch’s postulates, established in the pre-(meta)genomic era. The term “pathobiome” was coined to describe a consortium of microbial species that interact with each other and the host to foster pathogenicity and the development of a disease. The concept of the “pathobiome” originally emerged from research on the human microbiome and suggested that dysbiosis of a balanced and diverse microbial community structure is always aligned or correlated with an unhealthy condition. Similarly, a healthy plant is typically associated with a diverse and stable community structure, described as a symbiome, that plays an essential role in its growth and function. A shift from a symbiome to a pathobiome occurs during the onset of disease, and usually involves major compositional transitions leading to pathogen proliferation and disease development. As such, the pathobiome concept provides a more holistic and realistic view of disease development, where complex assemblages of organisms are involved.

In the context of postharvest disease, no efforts have been made thus far to identify and functionally investigate the pathobiome of postharvest diseases. We predict that understanding the symbiome and formation of the pathobiome will become a crucial element for mitigating disease in global food production systems. In a project led by Samir Droby, we apply genomic-based modeling approaches to explore the role of the microbial community in disease progression aiming at the development of guided strategies for chemical-free disease suppression. See more about this project on Rotem Bartuv's and Jenny Yusim's personal pages.  

4. Management of greenhouse gas emissions in natural and artificial systems.

We apply our computational approaches for the development of a model-guided simulation system for the management of greenhouse gas emissions in wetlands and waste-water treatments.

The project is a collaboration with Keren Yanuka-Golub ​(Galilee Society Institute of Applied Research), and impact NRS.

We are now recruiting students for these projects.

5. Development of new tools for the functional analysis of microbial interactions

NetMet serves the exploration of microbial activity in different environments.

NetCom is a pioneering web tool for the analysis of microbial interactions based on metagenomics data.