1. Modeling based design of soil amendments that lead to enhanced biodegradation of soil pollutants including BTEX and herbicides
In a recent series of works, we provided pioneering evidence, validated in-vitro and in-situ, that CBM-based predictions can guide the development of strategies for microbiome management where our case study concerned the optimization of selected function-enhanced biodegradation of agricultural pollutants. For example, Dhakar et al. demonstrated the use of CBM for optimizing the biodegradation of agricultural pollutants and for the educated design of stimulant-based targeting of specific native degrader-taxa. Xu et al. demonstrated the use of CBM for characterizing the exchange fluxes between primary degraders and co-inhabiting soil species that indirectly support the process of degradation of agricultural pollutants in soil.
Nowadays, we are testing the application of the approach towards several practical solutions including:
The development of commercial clean-up solutions for agricultural soil (In collaboration with Prof. Hanan Eizenberg and Prof. Zeev Ronen)
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.
An example of the design of a biostimulation strategy by specific microbial groups based on CBM. The simulation matrix estimates the simulating effect of each stimulant by four indigenous degraders. In situ validated prioritization of the compounds as biostimulants of specific taxonomic groups resulted in an overall accuracy of 0.75 and precision of 0.67. The example was taken from Dhakar et al.
2. Management of the soil microbiome in cropping agro-ecosystems
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.
In a recent study (by Berihu et al. 2023), we demonstrate 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. The project is a collaboration with Prof. Mark Mazzola and Tracey Somera. See more about this project on Maria Berihu's and Alon Ginatt's personal pages.
Watch the video explaining how we use metagenomics for reducing unpredictability in soil amendment research to benefit agricultural crops.
An example of metagenomic-based predictions of taxa-dependent functions in a beneficial root community (Figure is taken from Berihu et al., 2023)
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.
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.
The framework for NetCom is illustrated here (From Berihu et al., 2023).