Analysis of nonsynonymous SNPs in candidate genes that influence bovine temperament and evaluation of their effect in Brahman cattle

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Abstract

Background

Temperament is an important production trait in cattle and multiple strategies had been developed to generate molecular markers to assist animal selection. As nonsynonymous single nucleotide polymorphisms are markers with the potential to affect gene functions, they could be useful to predict phenotypic effects. Genetic selection of less stress-responsive, temperamental animals is desirable from an economic and welfare point of view.

Methods and results

Two nonsynonymous single nucleotide polymorphisms identified in HTR1B and SLC18A2 candidate genes for temperament were analyzed in silico to determine their effects on protein structure. Those nsSNPs allowing changes in proteins were selected for a temperament association analysis in a Brahman population. Transversion effects on protein structure were evaluated in silico for each amino acid change model, revealing structural changes in the proteins of the HTR1B and SLC18A2 genes. The selected nsSNPs were genotyped in a Brahman population ( n = 138), and their genotypic effects on three temperament traits were analyzed: exit velocity, pen score, and temperament score. Only the SNP rs209984404-HTR1B (C/A) showed a significant association ( P = 0.0144) with pen score. The heterozygous genotype showed a pen score value 1.17 points lower than that of the homozygous CC genotype.

Conclusion

The results showed that in silico analysis could direct the selection of nsSNPs with the potential to change the protein. Non-synonymous single nucleotide polymorphisms causing structural changes and reduced protein stability were identified. Only rs209984404-HTR1B shows that the allele affecting protein stability was associated with the genotype linked to docility in cattle.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11033-024-09264-4.

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Most cited references 34

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SWISS-MODEL: homology modelling of protein structures and complexes

Andrew Waterhouse, Martino Bertoni, Stefan Bienert … (2018)

Abstract Homology modelling has matured into an important technique in structural biology, significantly contributing to narrowing the gap between known protein sequences and experimentally determined structures. Fully automated workflows and servers simplify and streamline the homology modelling process, also allowing users without a specific computational expertise to generate reliable protein models and have easy access to modelling results, their visualization and interpretation. Here, we present an update to the SWISS-MODEL server, which pioneered the field of automated modelling 25 years ago and been continuously further developed. Recently, its functionality has been extended to the modelling of homo- and heteromeric complexes. Starting from the amino acid sequences of the interacting proteins, both the stoichiometry and the overall structure of the complex are inferred by homology modelling. Other major improvements include the implementation of a new modelling engine, ProMod3 and the introduction a new local model quality estimation method, QMEANDisCo. SWISS-MODEL is freely available at https://swissmodel.expasy.org.

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Structure validation by Calpha geometry: phi,psi and Cbeta deviation.

Simon C. Lovell, Ian W. Davis, W Bryan Arendall … (2003)

Geometrical validation around the Calpha is described, with a new Cbeta measure and updated Ramachandran plot. Deviation of the observed Cbeta atom from ideal position provides a single measure encapsulating the major structure-validation information contained in bond angle distortions. Cbeta deviation is sensitive to incompatibilities between sidechain and backbone caused by misfit conformations or inappropriate refinement restraints. A new phi,psi plot using density-dependent smoothing for 81,234 non-Gly, non-Pro, and non-prePro residues with B < 30 from 500 high-resolution proteins shows sharp boundaries at critical edges and clear delineation between large empty areas and regions that are allowed but disfavored. One such region is the gamma-turn conformation near +75 degrees,-60 degrees, counted as forbidden by common structure-validation programs; however, it occurs in well-ordered parts of good structures, it is overrepresented near functional sites, and strain is partly compensated by the gamma-turn H-bond. Favored and allowed phi,psi regions are also defined for Pro, pre-Pro, and Gly (important because Gly phi,psi angles are more permissive but less accurately determined). Details of these accurate empirical distributions are poorly predicted by previous theoretical calculations, including a region left of alpha-helix, which rates as favorable in energy yet rarely occurs. A proposed factor explaining this discrepancy is that crowding of the two-peptide NHs permits donating only a single H-bond. New calculations by Hu et al. [Proteins 2002 (this issue)] for Ala and Gly dipeptides, using mixed quantum mechanics and molecular mechanics, fit our nonrepetitive data in excellent detail. To run our geometrical evaluations on a user-uploaded file, see MOLPROBITY (http://kinemage.biochem.duke.edu) or RAMPAGE (http://www-cryst.bioc.cam.ac.uk/rampage). Copyright 2003 Wiley-Liss, Inc.

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I-Mutant2.0: predicting stability changes upon mutation from the protein sequence or structure

Emidio Capriotti, Piero Fariselli, Rita Casadio (2005)

I-Mutant2.0 is a support vector machine (SVM)-based tool for the automatic prediction of protein stability changes upon single point mutations. I-Mutant2.0 predictions are performed starting either from the protein structure or, more importantly, from the protein sequence. This latter task, to the best of our knowledge, is exploited for the first time. The method was trained and tested on a data set derived from ProTherm, which is presently the most comprehensive available database of thermodynamic experimental data of free energy changes of protein stability upon mutation under different conditions. I-Mutant2.0 can be used both as a classifier for predicting the sign of the protein stability change upon mutation and as a regression estimator for predicting the related ΔΔG values. Acting as a classifier, I-Mutant2.0 correctly predicts (with a cross-validation procedure) 80% or 77% of the data set, depending on the usage of structural or sequence information, respectively. When predicting ΔΔG values associated with mutations, the correlation of predicted with expected/experimental values is 0.71 (with a standard error of 1.30 kcal/mol) and 0.62 (with a standard error of 1.45 kcal/mol) when structural or sequence information are respectively adopted. Our web interface allows the selection of a predictive mode that depends on the availability of the protein structure and/or sequence. In this latter case, the web server requires only pasting of a protein sequence in a raw format. We therefore introduce I-Mutant2.0 as a unique and valuable helper for protein design, even when the protein structure is not yet known with atomic resolution. Availability: .

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Author and article information

Contributors

Ana María Sifuentes-Rincón:

ORCID: http://orcid.org/0000-0002-3867-9886

asifuentes@ipn.mx

Journal

Journal ID (nlm-ta): Mol Biol Rep

Journal ID (iso-abbrev): Mol Biol Rep

Title: Molecular Biology Reports

Publisher: Springer Netherlands (Dordrecht )

ISSN (Print): 0301-4851

ISSN (Electronic): 1573-4978

Publication date (Electronic): 7 February 2024

Publication date PMC-release: 7 February 2024

Publication date (Print): 2024

Volume: 51

Issue: 1

Electronic Location Identifier: 285

Affiliations

[1 ]Laboratorio de Biotecnología Animal, Centro de Biotecnología Genómica, Instituto Politécnico Nacional, ( https://ror.org/059sp8j34) Reynosa, Tamaulipas, 88710 México

[2 ]Unidad Académica Multidisciplinaria Mante, Universidad Autónoma de Tamaulipas, ( https://ror.org/04hhneb29) El Mante, Tamaulipas, 89840 México

[3 ]GRID grid.512856.d, ISNI 0000 0000 8863 1587, National Animal Disease Center, Agricultural Research Service, United States Department of Agriculture, ; Ames, IA 50010 USA

[4 ]Department of Animal Science, Texas A&M University, ( https://ror.org/01f5ytq51) College Station, TX 77843 USA

[5 ]GRID grid.264756.4, ISNI 0000 0004 4687 2082, Texas A&M AgriLife Research, ; Overton, TX 75684 USA

Author information

Gilberto Ruiz-De-La-Cruz http://orcid.org/0000-0003-1401-1192

Ana María Sifuentes-Rincón http://orcid.org/0000-0002-3867-9886

Francisco Alejandro Paredes-Sánchez http://orcid.org/0000-0003-2661-6607

Gaspar Manuel Parra-Bracamonte http://orcid.org/0000-0002-9327-2042

Eduardo Casas http://orcid.org/0000-0003-3773-4035

David G. Riley http://orcid.org/0000-0003-2353-7823

George A. Perry http://orcid.org/0000-0002-0102-9046

Thomas H. Welsh Jr. http://orcid.org/0000-0003-2212-172X

Ronald D. Randel http://orcid.org/0000-0001-7355-2752

Article

Publisher ID: 9264

DOI: 10.1007/s11033-024-09264-4

PMC ID: 10850011

PubMed ID: 38324050

SO-VID: 8bd3d4cc-c763-4650-9757-181bfcdd3c17

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 21 December 2023

Date accepted : 17 January 2024

Funding

Funded by: Consejo Nacional de Humanidades, Ciencias y Tecnología

Award ID: 294826 and 299055

Funded by: FundRef http://dx.doi.org/10.13039/501100007161, Secretaría de Investigación y Posgrado, Instituto Politécnico Nacional;

Award ID: SIP 20230271

Custom metadata

ScienceOpen disciplines: Molecular biology

Keywords: behavior,candidate genes,modelling,amino acid change,serotonin receptor

Data availability:

ScienceOpen disciplines: Molecular biology

Keywords: behavior, candidate genes, modelling, amino acid change, serotonin receptor

Analysis of nonsynonymous SNPs in candidate genes that influence bovine temperament and evaluation of their effect in Brahman cattle

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Abstract

Background

Methods and results

Conclusion

Supplementary Information

Related collections

G3: Genes|Genomes|Genetics

Most cited references 34

SWISS-MODEL: homology modelling of protein structures and complexes

Structure validation by Calpha geometry: phi,psi and Cbeta deviation.

I-Mutant2.0: predicting stability changes upon mutation from the protein sequence or structure

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