USGS Science⌕ Search
Home/Archive/USGS

USGS · 70247358

Amino acid variation at the mitochondrial binding site of Antimycin A is proposed to reflect sensitivity and toxicity differences among fish species

Brooke A. Baudoin·Bonnie L. Brown·Robin D. Calfee·Jill Jenkins
View original source ↗Export metadata ↓

Abstract

To better understand differential sensitivities among fish species to the piscicidal compound Antimycin-A (ANT-A), we hypothesized that variations in amino acids at the ANT-A binding site may reflect toxicity differences. Protein sequences for six motifs comprising the ANT-A binding site were obtained and compared for invasive carp species ( N = 515) and seven non-target species ( N = 277); a consensus was delineated from each species. The carp species, Common Carp ( Cyprinus carpio ), Silver Carp ( Hypophthalmichthys molitrix ), Bighead Carp ( Hypophthalmichthys nobilis ), Grass Carp ( Ctenopharyngodon idella ), and Black Carp ( Mylopharyngodon piceus ) , showed the same amino acids at the site; thus, it was termed the carp consensus motif sequence (CCM). Channel Catfish ( Ictalurus punctatus ) showed the most amino acid polymorphisms, with three motifs 96–100% different from CCM. Within a species, Bluegill ( Lepomis macrochirus ) and Fathead Minnow ( Pimephales promelas ) variation per motif was most dissimilar (46.7% and 21.6%, respectively). Organismal mortality data from the literature indicated Yellow Perch ( Perca flavescens ) , Walleye ( Sander vitreus ) , and American Gizzard Shad ( Dorosoma cepedianum ) to be most sensitive to the piscicide, Catfish least sensitive, and all others intermediate. The protein sequence variations of the binding site appeared to be in accord with organismal sensitivity categories when they differed from the CCM; the motifs in Gizzard Shad and Walleye were the same as in CCM. The physical/chemical nature of ANT-A is important to consider in organismal response comparisons. This cellular approach of studying ANT-A binding at its target enzyme is a non-destructive way to predict piscicidal efficacy of ANT-A against fishes of interest, informs management decisions in control efforts for invasives, and can be used to forecast effects on sympatric species.

Explore related subjects

FishesArticle
90° N90° S · 180° W ← longitude → 180° E
Source-reported bounding extent: 25.08° to 49.38905° latitude; -124.68721° to -66.96466° longitude. This indicates report coverage, not an exact sampling location. View area on OpenStreetMap.

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Brooke A. Baudoin, Bonnie L. Brown, Robin D. Calfee, Jill Jenkins. 2023-07-22. Amino acid variation at the mitochondrial binding site of Antimycin A is proposed to reflect sensitivity and toxicity differences among fish species. https://doi.org/10.3390/fishes8070381

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

USGS2026

Diet and trophic ecology of invasive Blue Catfish Ictalurus furcatus in an eastern shore tributary of the Chesapeake Bay

Blue Catfish Ictalurus furcatus were first introduced to Virginia tributaries of the Chesapeake Bay in the 1970s and have greatly expanded in population size and distribution, prompting ecological and economic concerns. This study investigates the diet and trophic ecology of invasive Blue Catfish in tidal, low-salinity reaches of the Nanticoke River and Marshyhope Creek, on the Chesapeake Bay’s Eastern Shore (Maryland, USA), using complementary stomach content (SCA) and stable isotope (SIA) analyses. Stomach contents from Blue Catfish ( n = 363 non-empty stomachs) were collected opportunistically from October 2021 through November 2022. For SIA, 136 individuals across 21 fish species, blue crab Callinectes sapidus , and arrow arum Peltandra virginica were sampled between October 2021 and October 2023. SCA revealed considerable seasonal and size-based diet variability, suggesting a shift from omnivory to piscivory at 563 mm total length. Primary prey included fish, arrow arum seeds, Asian clams Corbicula fluminea , and amphipods, along with seasonally available species of economic and ecological importance (river herring Alosa spp., blue crab, White Perch Morone americana , and Atlantic Menhaden Brevoortia tyrannus ). Minimal isotopic niche overlap between large and small Blue Catfish size classes, alongside ontogenetic trends in δ 13 C and δ 15 N, substantiated SCA-detected diet shifts. Trophic level estimates derived from Blue Catfish δ 15 N ( n = 54 individuals) were higher than those from SCA, particularly for individuals <563 mm. Blue Catfish occupied a relatively high trophic level, and carbon and sulfur SIA indicated marine resource utilization, substantiating SCA results. This research describes localized trophic interactions of invasive Blue Catfish to inform fishery management planning regarding Blue Catfish’s potential influence on other species of management interest.

Delaware, Maryland↗
USGS2025

Assessing fish growth changes under an ecosystem regime shift: The approach of linear mixed-effects modeling with application to Lake Huron lake trout

Linear mixed-effects modeling has been used to characterize fish growth changes over time, but early studies did not fully consider the implicit relationship among the three major factors of fish age, year-class, and sampling year in model development. Our objective is to resolve this issue and develop a linear mixed-effects model to clearly assess how lake trout ( Salvelinus namaycush ) growth has responded to the ecosystem regime shift in Lake Huron. We found that, from the late 1970s through the early 2000s, lake trout weight-at-age in northern Lake Huron had large variation with no apparent temporal trend, whereas the weight-at-age in southern Lake Huron steadily declined but remained larger than that in northern Lake Huron. The difference between northern and southern Lake Huron almost disappeared prior to the 2003 collapse of alewives, which accelerated the weight-at-age declines during the 2000s in both regions. By 2010, lake trout growth had a modest recovery and then stabilized. Our model fitted and explained variation in lake trout weight-at-age between years, regions, and recruitment origins, with the fixed age effect representing the general growth trajectory, the fixed year effect as the year-specific growth index, and the random year-class effect as the cohort-specific growth index.

Lake Huron↗
USGS2025

Comparing year-class strength indices from longitudinal analysis of catch-at-age data with those from catch-curve regression: Application to Lake Huron lake trout

Fish year-class strength (YCS) has been estimated via longitudinal analysis of catch-at-age data and via catch-curve regression, but no study has compared the two approaches. The objective of this study was to compare YCS estimates derived from both approaches applied to catch-at-age data for the lake trout (Salvelinus namaycush) population in the main basin of Lake Huron, one of the Laurentian Great Lakes of North America. YCS was reconstructed for both hatchery-stocked and wild lake trout. Akaike information criterion (AIC) and Bayesian information criterion (BIC) were used to compare 14 linear mixed-effects models for longitudinal analysis of catch-at-age data, and three linear mixed-effects models for catch-curve regression. From the best models based on AIC or BIC comparisons, YCS estimates with year-class as a fixed effect were consistent with those estimated with year-class as a random effect. Patterns and trends in the YCS estimates were also the same or similar between the longitudinal analysis of catch-at-age data approach and the catch-curve regression approach, suggesting that both modeling approaches are applicable to a variety of fish populations. indicating that both approaches provide robust measures of YCS. Potential bias in using the approach of catch-curve regression could be caused by abrupt changes in adult mortality. It is also critical to recognize multiple recruitment origins for using the approach of longitudinal analysis of catch-at-age data.

Lake Huron↗