Fatty acid methyl ester (FAME) profiling for species-specific characterization and detection of fungal pathogens that cause tree and grapevine trunk diseases

Fungal trunk diseases are of major concern for tree fruit, nut, and grape growers throughout the world. These diseases include Eutypa dieback of grape, caused by <i>Eutypa lata</i>, band canker of almond, caused by <i>Neofusicoccum mediterraneum</i> and <i>Neofusicoccum parvum</i>, and twig and branch dieback of walnut, caused by <i>N. mediterraneum</i>, Botryosphaeria dieback of grape, caused by <i>Diplodia mutila, Diplodia seriata, N. mediterraneum</i>, and <i>N. parvum</i>, and esca of grape, caused by <i>Phaeomoniella chlamydospora</i> and <i>Phaeoacremonium minimum</i>. Given the common occurrence of mixed infections, and the similar wood symptoms at the macroscopic level, species-specific detection tools are needed. Fatty acid methyl ester (FAME) profiling can be an effective and inexpensive diagnostic tool. FAME analyses were conducted on pure cultures of multiple isolates per species to characterize profiles and assess whether this technique could result in consistent identification. FAME profiles were dominated by oleic acid (18:1 ω9c) and palmitic acid (16:0), with less abundant FAMEs in different ratios for each species and isolates within species. Canonical discriminant analyses revealed which minor FAMEs were most variable, with a total of 20 different FAMEs that can explain 69.01% of profile variance in the first two canonicals. Using these analyses, samples were self-tested and correctly sorted 97.18% of the time. Within species, canonical discriminant analyses were able to separate isolates further, often by original geographic location or by host plant species. These results further suggest that potential novel species, subspecies, or races may be present among the isolates analyzed, demonstrating the capacity of FAME profiling to have a role in discovering cryptic species and accurately identifying fungal pathogens in conjunction with other molecular techniques and genomic analyses.