Biology subjects

Ronen, Z.

Publications and source records attributed to Ronen, Z..

3 recordsLinked to original sources

bioRxiv2023

Metabolic adaptations and aquifer connectivity underpin the high productivity rates in the relict subsurface water

BackgroundDiverse microbes catalyze biogeochemical cycles in the terrestrial subsurface, yet the corresponding ecophysiology was only estimated in a limited number of subterrestrial, often shallow aquifers. Here, we detrained the productivity, diversity, and functions of active microbial communities in the Judea Group carbonate and the underlying deep (up to 1.5 km below ground) Kurnub Group Nubian sandstone aquifers. These pristine oligotrophic aquifers, recharged more than tens to hundreds of thousands years ago, contain fresh/brackish, hypoxic/anoxic, often hot (up to 60{degrees}C) water and serve as habitats for key microbial producers. ResultsWe show that recent groundwater recharge, inorganic carbon and ammonium strongly influence chemosynthetic primary productivity in carbonate and sandstone aquifers (4.4-21.9 {micro}g C d-1 L-1 and 1.2-2.7 {micro}g C d-1 L-1, respectively). These high values indicate the possibility that the global aquifer productivity rates may be underestimated. Metagenome analysis revealed the prevalence of chemoautotrophic pathways, particularly the Calvin-Benson-Bassham cycle. The key chemosynthetic lineages in the carbonate aquifer were Halothiobacillales, whereas Burkholderiales and Rhizobiales occupied the sandstone aquifer. Most chemosynthetic microbes may oxidize sulfur compounds or ammonium, using oxygen or oxidized nitrogen as electron acceptors. Abundant sulfate reducers in the anoxic deeper aquifer have the potential to catabolize various organics, fix carbon via the Wood Ljungdahl pathway, and often possess nitrogenase, indicating diazotrophic capabilities. Our data suggest that connectivity between the aquifers and their exposure to energy inputs and surface water may play a key role in shaping these communities, altering physicochemical parameters and selecting taxa and functions. We highlight the metabolic versatility in the deep subsurface that underpins their efficient harnessing of carbon and energy from different sources.

microbiology↗

bioRxiv2023

Mitigation of Antimicrobial Resistance Genes in Greywater Treated at Household Level

Greywater often contains microorganisms carrying antimicrobial resistance genes (ARGs). Reuse of greywater thus potentially facilitates the enrichment and spread of multidrug resistance, posing a possible hazard for communities that use it. As water reuse becomes increasingly necessary, it is imperative to determine how greywater treatment impacts ARGs. In this study, we characterize ARG patterns in greywater microbial communities before and after treatment by a recirculating vertical flow constructed wetland (RVFCW). This greywater recycling method has been adopted by some small communities and households for greywater treatment; however, its ability to remove ARGs is unknown. We examined the taxonomic and ARG compositions of microbial communities in raw and treated greywater from five households using shotgun metagenomic sequencing. Total ARGs decreased in abundance and diversity in greywater treated by the RVFCW. In parallel, the microbial communities decreased in similarity in treated greywater. Potentially pathogenic bacteria associated with antimicrobial resistance and mobile genetic elements were detected in both raw and treated water, with a decreasing trend after treatment. This study indicates that RVFCW systems have the potential to mitigate antimicrobial resistance-related hazards when reusing treated greywater, but further measures need to be taken regarding persistent mobile ARGs and potential pathogens.

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↗