Duration of postdoc project:

Postdoc project leader:

Jan Zrimec (SICRIS, ResearchGate)

Link:

SICRIS

Abstract: 

Antimicrobial drugs either directly kill microorganisms or prevent their growth and reproduction. Microbes respond to the drugs by development of antimicrobial
resistance (AMR), thereby reducing their effects. Since the genes for AMR can spread between microbes via horizontal gene transfer (HGT), the excessive use of antimicrobial drugs in the last decades has resulted in the emergence of a growing number of drug resistant microbes (e.g. MRSA). Due to the frequency of such infections, AMR is currently one of the three largest global health problems, as only in Europe 25.000 deaths occur each year. Nevertheless, pharmaceutical companies practically no longer develop new antimicrobials, which has caused a shortage of new drugs. Therefore, based on new technologies and approaches, such as metagenomics, I propose to develop algorithms to accelerate the discovery of antimicrobials among natural compounds produced by microbes themselves.

Furthermore, by performing an indepth analysis of AMR in metagenomes, I will create innovative algorithms to optimize compounds and reduce the incidence of
AMR. The findings on very large diversity (> 10^6 / gram of soil) and high amount of unexplored microbial species (> 99%) show that they are an unexploited resource of natural compounds. However, although the genetic data of microbes is fully accessible using metagenomics approaches, methods must be developed to process this data in order to discover antimicrobial compounds that are coded inside it. Therefore, I will develop procedures for the discovery of potential new antimicrobial compounds in data libraries of metagenomic projects. Clusters of genes that code for biosynthesis of antimicrobial compounds will be identified by analysis of specific DNA regions and their structural properties. The process of characterization of gene clusters and their biosynthetic products according to reference data will be expanded by modeling based on evolutionary principles. By adapting the above methodology, AMR systems will be characterised and analysed to determine the potential for transfer of the AMR genes by HGT and for incidence of AMR in relation to the antimicrobials used.

Finally, the antimicrobial activity and AMR incidence of the discovered compounds will be predicted according to computed molecular properties on structureactivity relationships of the compounds. The expected results are (i) new antimicrobial compounds with a lower incidence of AMR than the ones currently used and (ii) validated tools that will be accessible to the general scientific community via the internet. The proposed project is organized into three interdependent work packages (WP). In WP 1, data will be acquired and interfaces constructed for further analysis. In WP 2, the core discovery procedure and algorithms will be developed. In WP 3, analysis of AMR systems will be performed and used to optimize antimicrobial compounds. Since the project will use of data from unexplored environments, a great number of new compounds can be accessed. Estimates indicate that less than 1% of all available microbial compounds have been identified. Consequently, I presume that there are many opportunities for the discovery of useful antimicrobials. The algorithms and procedures that will be developed are expected to have a wide applicability in the biotechnology and microbiology fields due to their flexibility and easy adaption for use not only with prokaryotic but also eukaryotic organisms, such as fungi and plants. However, the proposed analysis of AMR systems and development of strategies to lower the incidence of AMR has potentially the largest scientific as well as social impact. Such methods and strategies can help create solutions to improve healthcare and save lives in the fastest time as well as the greatest scope. The project can thus have an exceptional impact on science, the society as well as economy and is of the highest interest to Slovenia, the EU as well as globally.