Biology subjects

Ofaim, S.

Publications and source records attributed to Ofaim, S..

4 recordsLinked to original sources

bioRxiv2022

Genomics-based annotations help unveil the molecular composition of edible plants

Given the important role food plays in health and well-being, the past decades have seen considerable experimental efforts dedicated to mapping the chemical composition of food ingredients. As the composition of raw food is genetically predetermined, here we ask, to what degree can we rely on genome-associated metabolic annotations to predict the chemical composition of natural ingredients? To that end, we developed an approach to unveil the chemical composition of 75 edible plants genomes, finding that genome-associated functional annotations substantially increase the number of compounds linked to specific plants. To account for the gap between the metabolic potential represented by functional annotations and the compounds likely to be accumulated and detected experimentally, we employed a predictive thermodynamic feasibility approach to systematically identify compounds that are likely to accumulate in plants. To quantify the accuracy of our predictions, we performed untargeted metabolomics on 13 plants, allowing us to experimentally confirm the detectability of the predicted compounds. For example, we observed 59 potentially novel compounds predicted and experimentally detected in corn. Our study shows that the application of genome-associated functional annotations can lead to an integrated metabologenomics platform capable of unveiling the chemical composition of edible plants and the biochemical pathways responsible for the observed compounds. Author SummaryNutrition and well-being take a central role in todays high pace life, but how much do we really know about the food we eat? Here, we harness existing metabolic knowledge encrypted in staple food ingredients genome to help us explore the composition of raw edible plants. We first show the benefit and value of looking into genome-associated functional annotations on a wide scale. Next, we rely on new experimental data to develop a framework that helps us reveal new, potentially bioactive compounds in staple food ingredients. This has significance in, first, extending current food composition knowledge and second in discovering newly detected bioactive compounds, shedding light on the potential impacts of common food ingredients beyond their nutritional value as described in food labels. Finally, we show that staple foods that are already included in our daily diets might have the potential to contribute to our well-being.

bioinformatics↗

bioRxiv2020

Dynamic allocation of carbon storage and nutrient-dependent exudation in a revised genome-scale model of Prochlorococcus

Microbial life in the oceans impacts the entire marine ecosystem, global biogeochemistry and climate. The marine cyanobacterium Prochlorococcus, an abundant component of this ecosystem, releases a significant fraction of the carbon fixed through photosynthesis, but the amount, timing and molecular composition of released carbon are still poorly understood. These depend on several factors, including nutrient availability, light intensity and glycogen storage. Here we combine multiple computational approaches to provide insight into carbon storage and exudation in Prochlorococcus. First, with the aid of a new algorithm for recursive filling of metabolic gaps (ReFill), and through substantial manual curation, we extended an existing genome-scale metabolic model of Prochlorococcus MED4. In this revised model (iSO595), we decoupled glycogen biosynthesis/degradation from growth, thus enabling dynamic allocation of carbon storage. In contrast to standard implementations of flux balance modeling, we made use of forced influx of carbon and light into the cell, to recapitulate overflow metabolism due to the decoupling of photosynthesis and carbon fixation from growth during nutrient limitation. By using random sampling in the ensuing flux space, we found that storage of glycogen or exudation of organic acids are favored when the growth is nitrogen limited, while exudation of amino acids becomes more likely when phosphate is the limiting resource. We next used COMETS to simulate day-night cycles and found that the model displays dynamic glycogen allocation and exudation of organic acids. The switch from photosynthesis and glycogen storage to glycogen depletion is associated with a redistribution of fluxes from the Entner-Doudoroff to the Pentose Phosphate pathway. Finally, we show that specific gene knockouts in iSO595 exhibit dynamic anomalies compatible with experimental observations, further demonstrating the value of this model as a tool to probe the metabolic dynamic of Prochlorococcus.

microbiology↗

bioRxiv2019

Genome-Scale reconstruction of Paenarthrobacter aurescens TC1 metabolic model towards the study of atrazine bioremediation

Atrazine is an herbicide and pollutant of great environmental concern that is naturally biodegraded by microbial communities. The efficiency of biodegradation can be improved through the stimulating addition of fertilizers, electron acceptors, etc. In recent years, metabolic modelling approaches have become widely used as an in silico tool for organism-level phenotyping and the subsequent development of metabolic engineering strategies including biodegradation improvement. Here, we constructed a genome scale metabolic model, iRZ960, for Paenarthrobacter aurescens TC1 - a widely studied atrazine degrader - aiming at simulating its degradation activity. A mathematical stoichiometric metabolic model was constructed based on a published genome sequence of P. aurescens TC1. An Initial draft model was automatically constructed using the RAST and KBase servers. The draft was developed into a predictive model through semi-automatic gap-filling procedures including manual curation. In addition to growth predictions under different conditions, model simulations were used to identify optimized media for enhancing the natural degradation of atrazine without a need in strain design via genetic modifications. Model predictions for growth and atrazine degradation efficiency were tested in myriad of media supplemented with different combinations of carbon and nitrogen sources that were verified in vitro. Experimental validations support the reliability of the models predictions for both bacterial growth (biomass accumulation) and atrazine degradation. Predictive tools, such as the presented model, can be applied for achieving optimal biodegradation efficiencies and for the development of ecologically friendly solutions for pollutant degradation in changing environments.

bioinformatics↗

bioRxiv2016

Modeling trophic dependencies and exchanges among insects’ bacterial symbionts in a host-simulated environment

Individual organisms are linked to their communities and ecosystems via metabolic activities. Metabolic exchanges and co-dependencies have long been suggested to have a pivotal role in determining community structure. Metabolic interactions with bacteria have been key drivers in the evolution of sap-feeding insects, enabling complementation of their deprived nutrition. The sap-feeding whitefly Bemisia tabaci (Hemiptera: Aleyrodidae) harbors an obligatory symbiotic bacterium, as well as varying combinations of facultative symbionts. We took advantage of the well-defined bacterial community in B. tabaci as a case study for a comprehensive and systematic survey of metabolic interactions within the bacterial community and their associations with documented frequency of bacterial combinations. We first reconstructed the metabolic networks of five common B. tabaci symbionts (Portiera, Rickettsia, Hamiltonella, Cardinium and Wolbachia), and then used network analysis approaches to predict: (1) species-specific metabolic capacities in a simulated bacteriocyte-like environment; (2) metabolic capacities of the corresponding species combinations, and (3) dependencies of each species on different media components.\n\nThe automatic-based predictions for metabolic capacities of the symbionts in the host environment were in general agreement with previously reported genome analyses, each focused on the single-species level. The analysis suggested several previously un-reported routes for complementary interactions. Highly abundant symbiont combinations were found to have the potential to produce a diverse set of complementary metabolites, in comparison to un-detected combinations. No clear association was detected between metabolic codependencies and co-occurrence patterns. The findings indicate a potential key role for metabolic exchanges as key determinants shaping community structure in this system.

systems biology↗