Dataset Information

A Machine Learning Approach to Study Demographic Alterations in Honeybee Colonies Using SDS-PAGE Fingerprinting.

ABSTRACT: Honeybees, as social insects, live in highly organised colonies where tasks reflect the age of individuals. As is widely known, in this context, emergent properties arise from interactions between them. The accelerated maturation of nurses into foragers, stimulated by many negative factors, may disrupt this complex equilibrium. This complexity needs a paradigm shift: from the study of a single stressor to the study of the effects exerted by multiple stressors on colony homeostasis. The aim of this research is, therefore, to study colony population disturbances by discriminating overaged nurses from proper aged nurses and precocious foragers from proper aged foragers using SDS-PAGE patterns of haemolymph proteins and a machine-learning algorithm. The KNN (K Nearest Neighbours) model fitted on the forager dataset showed remarkably good performances (accuracy 0.93, sensitivity 0.88, specificity 1.00) in discriminating precocious foragers from proper aged ones. The main strength of this innovative approach lies in the possibility of it being deployed as a preventive tool. Depopulation is an elusive syndrome in bee pathology and early detection with the method described could shed more light on the phenomenon. In addition, it enables countermeasures to revert this vicious circle.

SUBMITTER: Cabbri R

PROVIDER: S-EPMC8233723 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A Machine Learning Approach to Study Demographic Alterations in Honeybee Colonies Using SDS-PAGE Fingerprinting.

Cabbri Riccardo R Ferlizza Enea E Bellei Elisa E Andreani Giulia G Galuppi Roberta R Isani Gloria G

Animals : an open access journal from MDPI 20210618 6

Honeybees, as social insects, live in highly organised colonies where tasks reflect the age of individuals. As is widely known, in this context, emergent properties arise from interactions between them. The accelerated maturation of nurses into foragers, stimulated by many negative factors, may disrupt this complex equilibrium. This complexity needs a paradigm shift: from the study of a single stressor to the study of the effects exerted by multiple stressors on colony homeostasis. The aim of th ...[more]

PMID: 34207270

Similar Datasets

Project description:Recombinant protein technology provides an invaluable source of proteins for use in structure-function studies, as immunogens, and in the development of therapeutics. Recombinant proteins are typically engineered with "tags" that allow the protein to be purified from crude host cell extracts using affinity based chromatography techniques. Amelogenin is the principal component of the developing enamel matrix and a frequent focus for biomineralization researchers. Several groups have reported the successful production of recombinant amelogenins but the production of recombinant amelogenin free of any tags, and at single band purity on silver stained SDS PAGE is technically challenging. This is important, as rigorous structure-function research frequently demands a high degree of protein purity and fidelity of protein sequence. Our aim was to generate His-tagged recombinant amelogenin at single band purity on silver stained SDS PAGE for use in functionality studies after His-tag cleavage. An acetic acid extraction technique (previously reported to produce recombinant amelogenin at 95% purity directly from E. coli) followed by repeated rounds of nickel column affinity chromatography, failed to generate recombinant amelogenin at single band purity. This was because following an initial round of nickel column affinity chromatography, subsequent cleavage of the His-tag was not 100% efficient. A second round of nickel column affinity chromatography, used in attempts to separate the cleaved His-tag free recombinant from uncleaved His-tagged contaminants, was still unsatisfactory as cleaved recombinant amelogenin exhibited significant affinity for the nickel column. To solve this problem, we used preparative SDS PAGE to successfully purify cleaved recombinant amelogenins to single band purity on silver stained SDS PAGE. The resolving power of preparative SDS PAGE was such that His-tag based purification of recombinant amelogenin becomes redundant. We suggest that acetic acid extraction of recombinant amelogenin and subsequent purification using preparative SDS PAGE provides a simple route to highly purified His-tag free amelogenin for use in structure-function experiments and beyond.

Dataset Information

A Machine Learning Approach to Study Demographic Alterations in Honeybee Colonies Using SDS-PAGE Fingerprinting.

Publications

A Machine Learning Approach to Study Demographic Alterations in Honeybee Colonies Using SDS-PAGE Fingerprinting.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets