Nutritional Status Influence On Acaricide Tolerance In Varroa Destructor: A Multi-Level Analysis Of Physiological And Proteomic Mechanisms
Department of Agriculture
@usgov.usda_gov_nutritional_status_influence_on_acaricide_tol_a076b6ee
Department of Agriculture
@usgov.usda_gov_nutritional_status_influence_on_acaricide_tol_a076b6ee
Varroa destructor are harmful ectoparasitic mites of Apis mellifera honey bees. Recently, high colony losses have been reported by U.S. beekeepers. The effects of mites, spurned on by the resistance of mites to conventional pesticides used by many U.S. beekeepers, is likely a key factor driving high colony losses. While molecular mechanisms granting resistance have been described, another, less studied factor is tolerance of Varroa mites to pesticide exposure depending on the mites’ 'feed state', or the nutritional status, which may depend on the host developmental stage (adult, pupa) the mite has fed upon at the time of pesticide exposure. Here we conducted over 500 laboratory bioassays exposing over 2,000 mites to three different conventional pesticides used for their control and fed prior to exposure on honey bee adult or pupal hosts. We analyzed the mites for their protein levels (proteomic analysis) and also the activity levels of key detoxification enzymes. Protein analyses showed mites surviving exposure to pesticides had over 10-fold higher level of host proteins and more active detoxification enzymes. The results highlight the importance of having knowledge of mites' nutritional 'feed state' when conducting pesticide laboratory research with Varroa mites.
Organization: Department of Agriculture
Last updated: 2025-09-02T23:44:12.161849
Tags: acaricide-tolerance, feed-state, nutritional-status, proteomics, varroa-destructor
CREATE TABLE experiment_1 (
"date_of_assay" TIMESTAMP,
"mite_number" BIGINT,
"sample_id" VARCHAR,
"treatment" VARCHAR,
"alivedead" VARCHAR,
"feed_state" VARCHAR
);CREATE TABLE experiment_2 (
"assay_set" BIGINT,
"date_month" VARCHAR,
"sample_id" VARCHAR,
"feed_state" VARCHAR,
"compound" VARCHAR,
"live_dead" VARCHAR,
"tube_wt" DOUBLE,
"tube_mite_wt" DOUBLE -- Tube + Mite Wt,
"mite_wt_mg" DOUBLE -- Mite Wt (mg),
"n__mites" BIGINT -- #mites,
"wt_per_mite" DOUBLE,
"protein_mg_ml" DOUBLE -- Protein (mg/mL),
"protein_mite" DOUBLE,
"protein_weight" DOUBLE,
"cdnb" DOUBLE,
"dcnb" DOUBLE,
"gst_total_cdnb_dcnb" DOUBLE -- GST Total (CDNB + DCNB),
"ache_mmoles_min_mg_prot_ache_mmoles_min_mg_prot" DOUBLE -- AChE (mmoles/min/mg Prot) AChE (mmoles/min/mg Prot),
"n_1_na_nmoles_mg_p" DOUBLE -- 1-NA (nmoles/mg P),
"n_2_na" DOUBLE -- 2-NA,
"general_esterase_1na_2na" DOUBLE,
"p450_activity_urfu_per_mg" DOUBLE,
"log_cdnb" DOUBLE,
"log_dcnb" DOUBLE,
"log_total_gst" DOUBLE,
"log_1_na" DOUBLE,
"log_2_na" DOUBLE,
"log_general_esterase_1na_2na" DOUBLE,
"log_ache" DOUBLE,
"log_p450_activity_urfu_per_mg" DOUBLE
);CREATE TABLE experiment_3 (
"samples" VARCHAR,
"treatment" VARCHAR,
"alive_dead" VARCHAR,
"dietary_feed_state" VARCHAR,
"total_proteins_absolute_numbers" BIGINT,
"varroa_destructor_protein_absolute_numbers" BIGINT,
"apis_mellifera_protein_absolute_numbers" BIGINT,
"total_proteins_percentage" BIGINT,
"varroa_destructor_protein_percentage" DOUBLE,
"apis_mellifera_protein_percentage" DOUBLE
);CREATE TABLE experiment_4 (
"n" BIGINT -- #,
"identified_proteins" VARCHAR,
"accession_number" VARCHAR,
"alternate_id" VARCHAR,
"molecular_weight" VARCHAR,
"t_test_p_0_00015" VARCHAR -- T-Test (p < 0.00015),
"fold_change_by_category" DOUBLE,
"k1_adult_alive" BIGINT,
"k3_adult_alive" BIGINT,
"k7_adult_alive" BIGINT,
"k11_adult_alive" BIGINT,
"k5_pupa_alive" BIGINT,
"k9_pupa_alive" BIGINT,
"k13_pupa_alive" BIGINT,
"k2_adult_dead" BIGINT,
"k4_adult_dead" BIGINT,
"k6_adult_dead" BIGINT,
"k8_adult_dead" BIGINT,
"k12_adult_dead" BIGINT,
"k10_pupa_dead" BIGINT,
"k14_pupa_dead" BIGINT
);Anyone who has the link will be able to view this.