Genome sequence analysis of native Xenorhabdus strains isolated from Entomopathogenic nematodes in Argentina

Entomopathogenic nematodes from the genus Steinernema (Nematoda: Steinernematidae) are capable of causing the rapid killing of insect hosts, facilitated by their association with symbiotic Gram-negative bacteria in the genus Xenorhabdus (Enterobacterales: Morganellaceae), positioning them as interesting candidate tools for the control of insect pests. In spite of this, only a limited number of species from this bacterial genus have been identified from their nematode hosts and their insecticidal properties documented. This study aimed to perform the genome sequence analysis of fourteen Xenorhabdus strains that were isolated from Steinernema nematodes in Argentina. All of the strains were found to be able of killing 7th instar larvae of Galleria mellonella (L.) (Lepidoptera: Pyralidae). Their sequenced genomes harbour 110 putative insecticidal proteins including Tc, Txp, Mcf, Pra/Prb and App homologs, plus other virulence factors such as putative nematocidal proteins, chitinases and secondary metabolite gene clusters for the synthesis of different bioactive compounds. Maximum-likelihood phylogenetic analysis plus average nucleotide identity calculations strongly suggested that three strains should be considered novel species. The species name for strains PSL and Reich (same species according to % ANI) is proposed as Xenorhabdus littoralis sp. nov., whereas strain 12 is proposed as Xenorhabdus santafensis sp. nov. In this work, we present a dual insight into the biocidal potential and diversity of the Xenorhabdus genus, demonstrated by different numbers of putative insecticidal genes and biosynthetic gene clusters, along with a fresh exploration of the species within this genus.

This research was funded by Ministerio de Ciencia, Tecnología e Innovación (FONCYT), Plan Argentina Innovadora 2020, grant number PICT 2017-0087, by the Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), grant number PIP 2017-2019 GRUPO INVES, the Instituto de Investigación of the Universidad Nacional de Villa María, grant number PIC 2017-2019 and by the Generalitat Valenciana (Spain), grant number PROMETEO/2020/010.