Biology subjects

Leipart, V.

Publications and source records attributed to Leipart, V..

5 recordsLinked to original sources

bioRxiv2025

AI-Enhanced Marker-Assisted Selection Concept for The Multifunctional Honey Bee (Hymenoptera: Apidea) Protein Vitellogenin (Vg)

Managed honey bees (Hymenoptera: Apidae: Apis mellifera L) have experienced unsustainably high rates of annual loss driven by several interacting factors, most notably pests, pathogens, pesticides, and poor nutrition. Breeding bee stocks that can cope with these challenges is a priority. Advanced molecular methods (marker-assisted selection, MAS) have enhanced the breeding efficiency of domesticated animals in recent years, but have not contributed strongly to honey bee stock improvements. This is largely because desirable traits of bees usually emerge from collective phenotypes of workers (sterile females) instead of from the breeding individuals (queens and male drones). For collective phenotypes, single genes typically have small, additive effects, so identifying impactful MAS targets is challenging. Here, we provide proof of concept for a new approach to honey bee breeding through MAS using the multifunctional protein Vitellogenin (Vg), a protein known to interact with and mitigate the primary drivers of colony loss. Our pipeline leverages cutting-edge, artificial intelligence (AI)-driven protein structure modeling algorithms to predict the effects of genetic variants of Vg on relevant molecular functions including lipid, zinc, and DNA binding. Following the AI-powered Vg variant selection step, we use a combination of standard apicultural techniques and DNA sequencing validation to breed honey bee queens homozygous for the desirable Vg allele. Our protocol can kick-start a new area of modernized bee breeding: an AI-enhanced MAS system that allows cost-effective and nimble development of stocks to meet urgent and long-term needs of stakeholders.

genetics↗

bioRxiv2025

Assessing structure-function impacts on Vitellogenin by leveraging allelic variant occurring in honey bee subspecies Apis mellifera meliffera

Computational advances involving artificial intelligence (AI) and successful experimental state-of-the-art structure determination can provide detailed pictures of large and complex protein structures, and their variations. A standout case is Vitellogenin (Vg) derived from the honey bee (Apis mellifera). Vg is an essential protein for reproduction in almost all egg-laying animals, and can in addition regulate behavior and provide immunological support in some species, including the honey bee. Information is limited in terms of the structure-function relationships that underlie Vgs pleiotropic functions, at least in part because this protein is not expressed in the best developed gene-editing models, such as fruit flies and mice. However, naturally occurring allelic variation in Vg can provide some insight, i.e., into which changes (mutations) are allowable (present at some frequency) vs. likely not allowable (not present or only at low frequency). Here, we leverage a unique dataset of 1,086 fully sequenced Vg alleles from honey bees in 15 countries. We identify a population-specific 9 nucleotide deletion in a locally endangered honey bee subspecies (A. m. mellifera) that impacts a loop structure in a central Vg domain. Due to the A. m. mellifera population history of near extinction and human intervention, an assessment of this Vg variant is not only theoretically interesting but also relevant for subspecies conservation efforts. Using structural bioinformatics, molecular dynamics simulations, and a transformer-based indel predictor (IndeLLM), we demonstrate that Vg protein structure and stability can be maintained despite the deletion. Our approach also reveals the dynamic nature of specific regions in Vg for the first time. Generalizable results may extend to other egg-laying animals of ecological and economic importance.

bioinformatics↗

bioRxiv2025

Leveraging protein language models and scoring function for Indel characterisation and transfer learning

1.Protein language models (PLMs) are increasingly used to assess the impact of genetic variation on proteins. By leveraging sequence information alone, PLMs achieve high performance and accuracy and can outperform traditional pathogenicity predictors specifically designed to identify harmful variants contributing to diseases. PLMs can perform zero-shot inference, making predictions without task-specific fine-tuning, offering a simpler and less overfitting-prone alternative to complex methods. However, studying in-frame insertions and deletions (indels) with PLMs remains challenging. Indels alter protein length, making direct comparisons between wildtype and mutant sequences not straightforward. Additionally, indel pathogenicity is less studied than other genetic variants, such as single nucleotide variants, resulting in a lack of annotated datasets. Despite these challenges, approaches that leverage PLMs through transfer learning have emerged, making it possible to capture the features needed for more accurate predictions. Still, the current approaches are limited in terms of allowed organisms, indel length, and interpretability. In this work, we devise a new scoring approach for indel pathogenicity prediction (IndeLLM) that provides a solution for the difference in protein lengths. Our method only uses sequence information and zero-shot inference with a fraction of computing time while achieving performances similar to other indel pathogenicity predictors. We used our approach to construct a simple transfer learning approach for a Siamese network, which outperformed all tested indel pathogenicity prediction methods (Matthews correlation coefficient = 0.77). IndeLLM is universally applicable across species since PLMs are trained on diverse protein sequences. To enhance accessibility, we designed a plug-and-play Google Colab notebook that allows easy use of IndeLLM and visualisation of the impact of indels on protein sequence and structure. The tool is available on GitHub https://github.com/OriolGraCar/IndeLLM and Colab https://colab.research.google.com/drive/1CgwprttaNFR_KeJGyFzP0a0C9Y wc4P. Graphical Abstract, if needed, or logo til include on Google Colab O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=159 SRC="FIGDIR/small/642715v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@e5d1b0org.highwire.dtl.DTLVardef@299b19org.highwire.dtl.DTLVardef@1859f02org.highwire.dtl.DTLVardef@18a7ba9_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2022

Where Honey Bee Vitellogenin may Bind Zn2+-Ions

The protein Vitellogenin (Vg) plays a central role in lipid transportation in most egg-laying animals. High Vg levels correlate with stress resistance and lifespan potential in honey bees (Apis mellifera). Vg is the primary circulating zinc-carrying protein in honey bees. Zinc is an essential metal ion in numerous biological processes, including the function and structure of many proteins. Measurements of Zn2+ suggest a variable number of ions per Vg molecule in different animal species, but the molecular implications of zinc-binding by this protein are not well understood. We used inductively coupled plasma mass spectrometry (ICP-MS) to determine that, on average, each honey bee Vg molecule binds 3 Zn2+-ions. Our full-length protein structure and sequence analysis revealed seven potential zinc-binding sites. These are located in the {beta}-barrel and -helical subdomains of the N-terminal domain, the lipid binding site, and the cysteine-rich C-terminal region of unknown function. Interestingly, two potential zinc-binding sites in the {beta}-barrel can support a proposed role for this structure in DNA-binding. Overall, our findings illustrate the capacity of honey bee Vg to bind zinc at several functional regions, indicating that Zn2+-ions are important for many of the activities of this protein. In addition to being potentially relevant for other egg-laying species, these insights provide a platform for studies of metal ions in bee health, which is of global interest due to recent declines in pollinator numbers.

biochemistry↗

bioRxiv2022

Identification of 121 variants of honey bee Vitellogenin protein sequences with structural differences at functional sites

Proteins are under selection to maintain central functions and to accommodate needs that arise in ever-changing environments. The positive selection and neutral drift that preserve functions result in a diversity of protein variants. The amount of diversity differs between proteins: multifunctional or disease-related proteins tend to have fewer variants than proteins involved in some aspects of immunity. Our work focuses on the extensively studied protein Vitellogenin (Vg), which in honey bees (Apis mellifera) is multifunctional and highly expressed and plays roles in immunity. Yet, almost nothing is known about the natural variation in the coding sequences of this protein or how amino acid-altering variants might impact structure-function relationships. Here, we map out allelic variation in honey bee Vg using biological samples from 15 countries. The successful barcoded amplicon Nanopore sequencing of 543 bees revealed 121 protein variants, indicating a high level of diversity in Vg. We find that the distribution of non-synonymous single nucleotide polymorphisms (nsSNPs) differs between protein regions with different functions; domains involved in DNA and protein-protein interactions contain fewer nsSNPs than the proteins lipid binding cavities. We outline how the central functions of the protein can be maintained in different variants and how the variation pattern may inform about selection from pathogens and nutrition.

biochemistry↗