Exploiting the chemical genomics of Pseudomonas spp. for natural product antifungals effective against fungal pathogens of wheat

Wheat is an important staple crop within global food production systems. It is the most widely cultivated cereal
in the world, with more than 220 million hectares planted annually, and the consumption of wheat accounting
for around 20% of total calories consumed globally. With an increasing global population, a major challenge
facing humanity is how agricultural systems will be able to rapidly increase food production, in line with
increasing demand. Fungal pathogens are a major contributor to the wheat 'yield gap', the difference between
the potential yield of crops and the actual yield harvested, with current estimates suggesting wheat yield losses
of around 21.5% exist due to pathogens and pests.

Zymoseptoria tritici as well as Aspergillus and Fusarium species, are causes of major fungal diseases of wheat,
causing threats to food security either by direct yield loss or through food contamination with mycotoxins,
with 5-10% of wheat containing Aspergillus or Fusarium mycotoxins above safe limits. Resistance to all known
classes of fungicides has arisen over the last 30 years, and new chemical control strategies are urgently needed.
Environmental bacterial isolates could potentially be an untapped source of naturally derived antifungal
compounds effective against these pathogens. Pseudomonas bacteria have shown great promise as a source
of novel antifungal secondary metabolites, with a proven ability to antagonise many fungal plant pathogens,
both in vitro and in planta, through the production of secondary metabolites.

The biosynthesis of secondary metabolites from localised clusters of genes, referred to as biosynthetic gene
clusters (BGCs) can be predicted from bacterial genome assemblies using software such as antiSMASH, utilising
hidden Markov models (HMM) rule-based detection. Combined with analytical chemistry approaches of the
interactions between Pseudomonas and fungal pathogens, predictive bioinformatics of secondary metabolites
offers...