Genetic profiles of ten Dirofilaria immitis isolates susceptible or resistant to macrocyclic lactone heartworm preventives.

BACKGROUND: For dogs and cats, chemoprophylaxis with macrocyclic lactone (ML) preventives for heartworm disease is widely used in the United States and other countries. Since 2005, cases of loss of efficacy (LOE) of heartworm preventives have been reported in the U.S. More recently, ML-resistant D. immitis isolates were confirmed. Previous work identified 42 genetic markers that could predict ML response in individual samples. For field surveillance, it would be more appropriate to work on microfilarial pools from individual dogs with a smaller subset of genetic markers.

METHODS: MiSeq technology was used to identify allele frequencies with the 42 genetic markers previously reported. Microfilaria from ten well-characterized new isolates called ZoeKY, ZoeMI, ZoeGCFL, ZoeAL, ZoeMP3, ZoeMO, ZoeAMAL, ZoeLA, ZoeJYD-34, and Metairie were extracted from fresh blood from dogs. DNA were extracted and sequenced with MiSeq technology. Allele frequencies were calculated and compared with the previously reported susceptible, LOE, and resistant D. immitis populations.

RESULTS: The allele frequencies identified in the current resistant and susceptible isolates were in accordance with the allele frequencies previously reported in related phenotypes. The ZoeMO population, a subset of the ZoeJYD-34 population, showed a genetic profile that was consistent with some reversion towards susceptibility compared with the parental ZoeJYD-34 population. The Random Forest algorithm was used to create a predictive model using different SNPs. The model with a combination of three SNPs (NODE_42411_RC, NODE_21554_RC, and NODE_45689) appears to be suitable for future monitoring.

CONCLUSIONS: MiSeq technology provided a suitable methodology to work with the microfilarial samples. The list of SNPs that showed good predictability for ML resistance was narrowed. Additional phenotypically well characterized D. immitis isolates are required to finalize the best set of SNPs to be used for large scale ML resistance screening.