Inhibitors of Reptile Venom Toxins

Bees originally developed their stinging apparatus and venom against members of their own species from other hives or against predatory insects. Nevertheless, the biological and biochemical response of arthropods to bee venom is not well studied. Thus, in this study, the physiological responses of a model insect species (American cockroach, Periplaneta americana) to honeybee venom were investigated. Bee venom toxins elicited severe stress (LD50 = 1.063 uL venom) resulting in a significant increase in adipokinetic hormones (AKHs) in the cockroach central nervous system and haemolymph. Venom treatment induced a large destruction of muscle cell ultrastructure, especially myofibrils and sarcomeres. Interestingly, co-application of venom with cockroach Peram-CAH-II AKH eliminated this effect. Envenomation modulated the levels of carbohydrates, lipids, and proteins in the haemolymph and the activity of digestive amylases, lipases, and proteases in the midgut. Bee venom significantly reduced vitellogenin levels in females. Dopamine and glutathione (GSH and GSSG) insignificantly increased after venom treatment. However, dopamine levels significantly increased after Peram-CAH-II application and after co-application with bee venom, while GSH and GSSG levels immediately increased after co-application. The results suggest a general reaction of the cockroach body to bee venom and at least a partial involvement of AKHs.

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Peer Review #1 of "Machine learning can differentiate venom toxins from other proteins having non-toxic physiological functions (v0.1)"

10.7287/peerj-cs.90v0.1/reviews/1 ◽

2016 ◽

Author(s):

JM Izarzugaza

Keyword(s):

Machine Learning ◽

Peer Review ◽

Physiological Functions ◽

Venom Toxins

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TOXIC AND IMMUNE ALLERGIC RESPONSES OF ANT VENOM TOXINS: A REVIEW

International Journal of Pharmacy and Pharmaceutical Sciences ◽

10.22159/ijpps.2021v13i11.42227 ◽

2021 ◽

pp. 1-7

Author(s):

SIMRAN SHARMA ◽

RAVI KANT UPADHYAY!

Keyword(s):

Immune Responses ◽

Biological Effects ◽

Therapeutic Agents ◽

Microbial Pathogens ◽

Intense Pain ◽

Venom Toxins ◽

Potential Source ◽

Ant Venom ◽

Peptide Toxins ◽

Promising Source

Present review article explains ant venom components and its allergic and biological effects in man and animals. Red ants or small fire ants secrete and inject venom very swiftly to defend their nest against predators, microbial pathogens, and competitors and to hunt the prey. Ant venom is a mixture of various organic compounds, including peptides, enzymes, and polypeptide toxins. It is highly toxic, allergic, invasive and venomous. It imposes sever paralytic, cytolytic, haemolytic, allergenic, pro-inflammatory, insecticidal, antimicrobial, and pain-producing pharmacologic activities after infliction. Victims show red ring-shaped allergic sign with regional swelling marked with intense pain. Ant venom also contains several hydrolases, oxidoreductases, proteases, Kunitz-like polypeptides, and inhibitor cysteine knot (ICK)-like (knottin) neurotoxins and insect defensins. Ant venom toxins/proteins generate allergic immune responses and employ eosinophils and produce Th2 cytokines, response. These compounds from ant venom could be used as a potential source of new anticonvulsants molecules. Ant venoms contain many small, linear peptides, an untapped source of bioactive peptide toxins. The remarkable insecticidal activity of ant venom could be used as a promising source of additional bio-insecticides and therapeutic agents.

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Machine learning can differentiate venom toxins from other proteins having non-toxic physiological functions

PeerJ Computer Science ◽

10.7717/peerj-cs.90 ◽

2016 ◽

Vol 2 ◽

pp. e90 ◽

Cited By ~ 24

Author(s):

Ranko Gacesa ◽

David J. Barlow ◽

Paul F. Long

Keyword(s):

Machine Learning ◽

Sequence Data ◽

Biological Data ◽

Biological Databases ◽

Web Based ◽

Physiological Functions ◽

Link Type ◽

Venom Toxins ◽

Venomous Animals ◽

Toxin Protein

Ascribing function to sequence in the absence of biological data is an ongoing challenge in bioinformatics. Differentiating the toxins of venomous animals from homologues having other physiological functions is particularly problematic as there are no universally accepted methods by which to attribute toxin function using sequence data alone. Bioinformatics tools that do exist are difficult to implement for researchers with little bioinformatics training. Here we announce a machine learning tool called ‘ToxClassifier’ that enables simple and consistent discrimination of toxins from non-toxin sequences with >99% accuracy and compare it to commonly used toxin annotation methods. ‘ToxClassifer’ also reports the best-hit annotation allowing placement of a toxin into the most appropriate toxin protein family, or relates it to a non-toxic protein having the closest homology, giving enhanced curation of existing biological databases and new venomics projects. ‘ToxClassifier’ is available for free, either to download (https://github.com/rgacesa/ToxClassifier) or to use on a web-based server (http://bioserv7.bioinfo.pbf.hr/ToxClassifier/).

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