V4, 18S and Operon eDNA workflows in arable soils
Soil-borne protist pathogens cause substantial crop losses, and early detection is critical if growers are to intervene before infection takes hold. Environmental DNA metabarcoding is a promising surveillance tool, but its reliability rests on choices of primer, marker region, sequencing platform, and bioinformatic pipeline that are rarely evaluated together. We compared three complete ribosomal workflows: a V4 amplicon sequenced using Illumina, and two longer regions (18S and Operon) sequenced using Oxford Nanopore Technologies. Two contrasting agricultural soils were investigated, including one with a confirmed clubroot infection. No single workflow recovered the full set of target pathogens, and the three proved complementary rather than redundant. In the soil with confirmed clubroot, the V4 and operon workflows detected Plasmodiophora brassicae, and this detection was maintained after all samples were rarefied to equal depth. The 18S workflow did not detect it, despite profiling the protist community in depth. Once a sequence was assigned to one of the target pathogen genera, species-level identification  was limited by reference-database coverage, regardless of the workflow used. The ITS region derived from the Operon revealed within-cluster variation that was not detected by 18S. Assigning that variation against a fungal-oriented database mostly left it unresolved rather than misassigned, at identities above 94.9%. Assigning the same sequences against a broader eukaryotic reference increased the number of sub-clusters with a correct genus-level name from five to nine of 15. Identification was therefore limited by reference coverage rather than by the region sequenced. Sub-cluster counts set an upper limit on diversity rather than counting lineages. These differences followed from the complete workflows, not from amplicon length or sequencing platform alone. We present this as a demonstration that these differences occur, rather than a ranking of the three workflows. Reliable surveillance will require combining workflows and expanding curated reference coverage.  All markers and the bioinformatic pipeline were validated using DNA from reference cultures. The medium 18S fragment yielded the highest proportion of protist reads, particularly for Stramenopiles (oomycetes), but missed other major clades. The short V4 fragment detected a broader range of pathogenic lineages, including P. brassicae, but lacked resolution for species-level assignments within Pythium. The long ribosomal operon fragment produced fewer reads but improved phylogenetic resolution.   These findings confirm the complementary strengths and limitations of each marker: shorter fragments maximize detection breadth, while longer fragments enhance taxonomic resolution. This study provides a methodological framework for optimizing eDNA-based monitoring of protist pathogens in agroecosystems.
Soil-borne protist pathogens cause substantial crop losses, and early detection is critical if growers are to intervene before infection takes hold. Environmental DNA metabarcoding is a promising surveillance tool, but its reliability rests on choices of primer, marker region, sequencing platform, and bioinformatic pipeline that are rarely evaluated together. We compared three complete ribosomal workflows: a V4 amplicon sequenced using Illumina, and two longer regions (18S and Operon) sequenced using Oxford Nanopore Technologies. Two contrasting agricultural soils were investigated, including one with a confirmed clubroot infection. No single workflow recovered the full set of target pathogens, and the three proved complementary rather than redundant. In the soil with confirmed clubroot, the V4 and operon workflows detected Plasmodiophora brassicae, and this detection was maintained after all samples were rarefied to equal depth. The 18S workflow did not detect it, despite profiling the protist community in depth. Once a sequence was assigned to one of the target pathogen genera, species-level identification  was limited by reference-database coverage, regardless of the workflow used. The ITS region derived from the Operon revealed within-cluster variation that was not detected by 18S. Assigning that variation against a fungal-oriented database mostly left it unresolved rather than misassigned, at identities above 94.9%. Assigning the same sequences against a broader eukaryotic reference increased the number of sub-clusters with a correct genus-level name from five to nine of 15. Identification was therefore limited by reference coverage rather than by the region sequenced. Sub-cluster counts set an upper limit on diversity rather than counting lineages. These differences followed from the complete workflows, not from amplicon length or sequencing platform alone. We present this as a demonstration that these differences occur, rather than a ranking of the three workflows. Reliable surveillance will require combining workflows and expanding curated reference coverage.  All markers and the bioinformatic pipeline were validated using DNA from reference cultures. The medium 18S fragment yielded the highest proportion of protist reads, particularly for Stramenopiles (oomycetes), but missed other major clades. The short V4 fragment detected a broader range of pathogenic lineages, including P. brassicae, but lacked resolution for species-level assignments within Pythium. The long ribosomal operon fragment produced fewer reads but improved phylogenetic resolution.   These findings confirm the complementary strengths and limitations of each marker: shorter fragments maximize detection breadth, while longer fragments enhance taxonomic resolution. This study provides a methodological framework for optimizing eDNA-based monitoring of protist pathogens in agroecosystems.