1
|
Stastna M. Advances in separation and identification of biologically important milk proteins and peptides. Electrophoresis 2024; 45:101-119. [PMID: 37289082 DOI: 10.1002/elps.202300084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 05/30/2023] [Accepted: 05/30/2023] [Indexed: 06/09/2023]
Abstract
Milk is a rich source of biologically important proteins and peptides. In addition, milk contains a variety of extracellular vesicles (EVs), including exosomes, that carry their own proteome cargo. EVs are essential for cell-cell communication and modulation of biological processes. They act as nature carriers of bioactive proteins/peptides in targeted delivery during various physiological and pathological conditions. Identification of the proteins and protein-derived peptides in milk and EVs and recognition of their biological activities and functions had a tremendous impact on food industry, medicine research, and clinical applications. Advanced separation methods, mass spectrometry (MS)-based proteomic approaches and innovative biostatistical procedures allowed for characterization of milk protein isoforms, genetic/splice variants, posttranslational modifications and their key roles, and contributed to novel discoveries. This review article discusses recently published developments in separation and identification of bioactive proteins/peptides from milk and milk EVs, including MS-based proteomic approaches.
Collapse
Affiliation(s)
- Miroslava Stastna
- Institute of Analytical Chemistry of the Czech Academy of Sciences, Brno, Czech Republic
| |
Collapse
|
2
|
Alguridi HI, Alzahrani F, Almalki S, Zamzami MA, Altayb HN. Identification and molecular docking of novel chikungunya virus NSP4 inhibitory peptides from camel milk proteins. J Biomol Struct Dyn 2023:1-16. [PMID: 37668009 DOI: 10.1080/07391102.2023.2254398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 08/28/2023] [Indexed: 09/06/2023]
Abstract
The chikungunya (CHIK) virus is an arbovirus belonging to the alphavirus (Togaviridae family). Around 85% of infected individuals suffer from symptoms such as high fever and severe joint pain; about 30 to 40% will develop a chronic joint illness. The Nsp4 protease is the most conserved protein in the alphavirus family and serves as an RNA-dependent RNA polymerase (RdRp). Targeting this enzyme might inhibit the CHIKV replication cycle. This work aims to in silico study the CHIKV RdRp inhibitory effect of peptides derived from camel milk protein as antiviral peptides. Various bioinformatics tools were recruited to identify, screen, predict and assess peptides obtained from camel milk as antiviral peptides (AVPs). During this study, CHIKV Nsp4 (polymerase) was used as a target to be inhibited by interaction with peptides derived from camel milk protein. Among 91 putative bioactive peptides, the best predicted 5 were further evaluated. Molecular docking showed that the top 5 AVPs generated better docking scores and interacted well with active sites of Nsp4 by the formation of different hydrogen bonds as well as other bonds. AVP63 and AVP20 showed the best Molecular docking and MD simulation results. The residue 315ASP of the GDD motif (catalytic core) exhibited a favorable interaction with the AVPs. The findings of this study suggest that the AVP20 derived from camel milk protein can be a potential novel CHIKV polymerase inhibitor.Communicated by Ramaswamy H. Sarma.
Collapse
Affiliation(s)
- Hassan I Alguridi
- Molecular Biology Department, Jeddah Regional Laboratory, Ministry of Health, Jeddah, Saudi Arabia
- Department of Biochemistry, Faculty of Sciences, King Abdulaziz University Jeddah, Saudi Arabia
- Research Unit, Jeddah Regional Laboratory, Ministry of Health, Jeddah, Saudi Arabia
| | - Faisal Alzahrani
- Department of Biochemistry, Faculty of Sciences, King Abdulaziz University Jeddah, Saudi Arabia
- Centre for Artificial Intelligence in Precision Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
- King Fahd Medical Research Center, Embryonic Stem Cells Unit, King Abdulaziz University Jeddah, Saudi Arabia
| | - Safar Almalki
- Molecular Biology Department, Jeddah Regional Laboratory, Ministry of Health, Jeddah, Saudi Arabia
- Laboratories and Blood Banks Administration, Ministry of Health, Jeddah, Saudi Arabia
| | - Mazin A Zamzami
- Department of Biochemistry, Faculty of Sciences, King Abdulaziz University Jeddah, Saudi Arabia
- Centre for Artificial Intelligence in Precision Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Hisham N Altayb
- Department of Biochemistry, Faculty of Sciences, King Abdulaziz University Jeddah, Saudi Arabia
- Centre for Artificial Intelligence in Precision Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| |
Collapse
|
3
|
Pauciullo A, Versace C, Gaspa G, Letaief N, Bedhiaf-Romdhani S, Fulgione A, Cosenza G. Sequencing and Characterization of αs2-Casein Gene ( CSN1S2) in the Old-World Camels Have Proven Genetic Variations Useful for the Understanding of Species Diversification. Animals (Basel) 2023; 13:2805. [PMID: 37685069 PMCID: PMC10487017 DOI: 10.3390/ani13172805] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 09/01/2023] [Accepted: 09/01/2023] [Indexed: 09/10/2023] Open
Abstract
The CSN1S2 gene encodes αs2-casein, the third most abundant protein in camel milk. Despite its importance in foals, human nutrition, and dairy processing, the CSN1S2 gene in camels has received little attention. This study presents the first complete characterization of the CSN1S2 gene sequence in Old-World camels (Camelus bactrianus and Camelus dromedarius). Additionally, the gene promoter, consisting of 752 bp upstream of exon 1, was analyzed. The entire gene comprises 17 exons, ranging in length from 24 bp (exons 4, 8, 11, and 13) to 280 bp (exon 17). Interesting was the identification of the exon 12 in both species. The promoter analysis revealed 24 putative binding sites in the Bactrian camel and 22 in dromedary camel. Most of these sites were typical elements associated with milk protein, such as C/EBP-α, C/EBP-β, Oct-1, and AP1. The SNP discovery showed relatively high genetic diversity compared to other camel casein genes (CSN1S1, CSN2, and CSN3), with a total of 34 polymorphic sites across the two species. Particularly noteworthy is the transition g.311G>A in the CSN1S2 promoter, creating a new putative consensus binding site for a C/EBP-β in the Bactrian camel. At the exon level, two novel variants were found. One was detected in exon 6 of the Bactrian camel (g.3639C>G), resulting in an amino acid replacement, p.36Ile>Met. The second variant was found in noncoding exon 17 of dromedary CSN1S2 (g.1511G>T). Although this mutation occurs in the 3'-UnTranslated Region, it represents the first example of exonic polymorphism in the CSN1S2 for this species. This SNP also affects the binding sites of different microRNAs, including the seed sequence of the miRNA 4662a-3p, highlighting its role as a regulatory factor for CSN1S2 gene. A PCR-RFLP was set up for genotyping a dromedary Tunisian population (n = 157), and the minor allele frequency was found to be 0.27 for the G allele, indicating a potential yield improvement margin. The interspersed elements (INEs) analysis revealed 10 INEs covering 7.34% and 8.14% of the CSN1S2 sequence in the Bactrian and dromedary camels, respectively. Furthermore, six elements (A, B, F, H, I, and L) are shared among cattle and camels and are partially found in other ruminants, suggesting a common ancestral origin of these retrotransposons. Conversely, elements C, D, E, and G are specific to camels.
Collapse
Affiliation(s)
- Alfredo Pauciullo
- Department of Agriculture, Forest and Food Sciences, University of Torino, 10095 Grugliasco, Italy
| | - Carmine Versace
- Department of Agriculture, Forest and Food Sciences, University of Torino, 10095 Grugliasco, Italy
| | - Giustino Gaspa
- Department of Agriculture, Forest and Food Sciences, University of Torino, 10095 Grugliasco, Italy
| | - Neyrouz Letaief
- Department of Agriculture, Forest and Food Sciences, University of Torino, 10095 Grugliasco, Italy
- Laboratory of Animal and Forage Production, National Agricultural Research Institute of Tunisia, University of Carthage, Ariana 1004, Tunisia
| | - Sonia Bedhiaf-Romdhani
- Laboratory of Animal and Forage Production, National Agricultural Research Institute of Tunisia, University of Carthage, Ariana 1004, Tunisia
| | - Andrea Fulgione
- Department of Agriculture, University of Napoli Federico II, 80055 Portici, Italy
| | - Gianfranco Cosenza
- Department of Agriculture, University of Napoli Federico II, 80055 Portici, Italy
| |
Collapse
|
4
|
Hoffmann M, Trummer N, Schwartz L, Jankowski J, Lee HK, Willruth LL, Lazareva O, Yuan K, Baumgarten N, Schmidt F, Baumbach J, Schulz MH, Blumenthal DB, Hennighausen L, List M. TF-Prioritizer: a Java pipeline to prioritize condition-specific transcription factors. Gigascience 2022; 12:giad026. [PMID: 37132521 PMCID: PMC10155229 DOI: 10.1093/gigascience/giad026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 02/23/2023] [Accepted: 04/05/2023] [Indexed: 05/04/2023] Open
Abstract
BACKGROUND Eukaryotic gene expression is controlled by cis-regulatory elements (CREs), including promoters and enhancers, which are bound by transcription factors (TFs). Differential expression of TFs and their binding affinity at putative CREs determine tissue- and developmental-specific transcriptional activity. Consolidating genomic datasets can offer further insights into the accessibility of CREs, TF activity, and, thus, gene regulation. However, the integration and analysis of multimodal datasets are hampered by considerable technical challenges. While methods for highlighting differential TF activity from combined chromatin state data (e.g., chromatin immunoprecipitation [ChIP], ATAC, or DNase sequencing) and RNA sequencing data exist, they do not offer convenient usability, have limited support for large-scale data processing, and provide only minimal functionality for visually interpreting results. RESULTS We developed TF-Prioritizer, an automated pipeline that prioritizes condition-specific TFs from multimodal data and generates an interactive web report. We demonstrated its potential by identifying known TFs along with their target genes, as well as previously unreported TFs active in lactating mouse mammary glands. Additionally, we studied a variety of ENCODE datasets for cell lines K562 and MCF-7, including 12 histone modification ChIP sequencing as well as ATAC and DNase sequencing datasets, where we observe and discuss assay-specific differences. CONCLUSION TF-Prioritizer accepts ATAC, DNase, or ChIP sequencing and RNA sequencing data as input and identifies TFs with differential activity, thus offering an understanding of genome-wide gene regulation, potential pathogenesis, and therapeutic targets in biomedical research.
Collapse
Affiliation(s)
- Markus Hoffmann
- Big Data in BioMedicine Group, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Freising D-85354, Germany
- Institute for Advanced Study, Technical University of Munich, Garching D-85748, Germany
- National Institute of Diabetes, Digestive, and Kidney Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Nico Trummer
- Big Data in BioMedicine Group, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Freising D-85354,Germany
| | - Leon Schwartz
- Big Data in BioMedicine Group, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Freising D-85354,Germany
| | - Jakub Jankowski
- National Institute of Diabetes, Digestive, and Kidney Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Hye Kyung Lee
- National Institute of Diabetes, Digestive, and Kidney Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Lina-Liv Willruth
- Big Data in BioMedicine Group, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Freising D-85354,Germany
| | - Olga Lazareva
- Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
- Junior Clinical Cooperation Unit, Multiparametric Methods for Early Detection of Prostate Cancer, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, 69117 Heidelberg, Germany
| | - Kevin Yuan
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Nina Baumgarten
- Institute of Cardiovascular Regeneration, Goethe University, 60590 Frankfurt am Main, Germany
- German Center for Cardiovascular Research, Partner site Rhein-Main, 60590 Frankfurt am Main, Germany
- Cardio-Pulmonary Institute, Goethe University Hospital, 60590 Frankfurt am Main, Germany
| | - Florian Schmidt
- Laboratory of Systems Biology and Data Analytics, Genome Institute of Singapore, 60 Biopolis Street, Singapore138672, Singapore
| | - Jan Baumbach
- Chair of Computational Systems Biology, University of Hamburg, Hamburg, Germany
- Computational BioMedicine Lab, University of Southern Denmark, Odense, Denmark
| | - Marcel H Schulz
- Institute of Cardiovascular Regeneration, Goethe University, 60590 Frankfurt am Main, Germany
- German Center for Cardiovascular Research, Partner site Rhein-Main, 60590 Frankfurt am Main, Germany
- Cardio-Pulmonary Institute, Goethe University Hospital, 60590 Frankfurt am Main, Germany
| | - David B Blumenthal
- Biomedical Network Science Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Lothar Hennighausen
- Institute for Advanced Study, Technical University of Munich, Garching D-85748, Germany
- National Institute of Diabetes, Digestive, and Kidney Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Markus List
- Big Data in BioMedicine Group, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Freising D-85354,Germany
| |
Collapse
|
5
|
Bruno S, Landi V, Senczuk G, Brooks SA, Almathen F, Faye B, Gaouar SSB, Piro M, Kim KS, David X, Eggen A, Burger P, Ciani E. Refining the Camelus dromedarius Myostatin Gene Polymorphism through Worldwide Whole-Genome Sequencing. Animals (Basel) 2022; 12:2068. [PMID: 36009658 PMCID: PMC9404819 DOI: 10.3390/ani12162068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 08/07/2022] [Accepted: 08/11/2022] [Indexed: 11/16/2022] Open
Abstract
Myostatin (MSTN) is a highly conserved negative regulator of skeletal muscle in mammals. Inactivating mutations results in a hyper-muscularity phenotype known as "double muscling" in several livestock and model species. In Camelus dromedarius, the gene structure organization and the sequence polymorphisms have been previously investigated, using Sanger and Next-Generation Sequencing technologies on a limited number of animals. Here, we carried out a follow-up study with the aim to further expand our knowledge about the sequence polymorphisms at the myostatin locus, through the whole-genome sequencing data of 183 samples representative of the geographical distribution range for this species. We focused our polymorphism analysis on the ±5 kb upstream and downstream region of the MSTN gene. A total of 99 variants (77 Single Nucleotide Polymorphisms and 22 indels) were observed. These were mainly located in intergenic and intronic regions, with only six synonymous Single Nucleotide Polymorphisms in exons. A sequence comparative analysis among the three species within the Camelus genus confirmed the expected higher genetic distance of C. dromedarius from the wild and domestic two-humped camels compared to the genetic distance between C. bactrianus and C. ferus. In silico functional prediction highlighted: (i) 213 differential putative transcription factor-binding sites, out of which 41 relative to transcription factors, with known literature evidence supporting their involvement in muscle metabolism and/or muscle development; and (ii) a number of variants potentially disrupting the canonical MSTN splicing elements, out of which two are discussed here for their potential ability to generate a prematurely truncated (inactive) form of the protein. The distribution of the considered variants in the studied cohort is discussed in light of the peculiar evolutionary history of this species and the hypothesis that extremely high muscularity, associated with a homozygous condition for mutated (inactivating) alleles at the myostatin locus, may represent, in arid desert conditions, a clear metabolic disadvantage, emphasizing the thermoregulatory and water availability challenges typical of these habitats.
Collapse
Affiliation(s)
- Silvia Bruno
- Department of Biosciences, Biotechnologies and Biopharmaceutics, University of Bari “Aldo Moro”, 70126 Bari, Italy
| | - Vincenzo Landi
- Department of Veterinary Medicine, University of Bari “Aldo Moro”, Valenzano, 70010 Bari, Italy
| | - Gabriele Senczuk
- Department of Agricultural, Environmental and Food Sciences, University of Molise, 86100 Campobasso, Italy
| | - Samantha Ann Brooks
- Department of Animal Sciences, University of Florida, Gainesville, FL 32610, USA
| | - Faisal Almathen
- Department of Public Health, College of Veterinary Medicine, King Faisal University, Al-Ahsa 31982, Saudi Arabia
- Camel Research Center, King Faisal University, Al-Ahsa 31982, Saudi Arabia
| | | | | | - Mohammed Piro
- Department of Medicine, Surgery and Reproduction, Institut Agronomique et Vétérinaire Hassan II, Rabat BP 6202, Morocco
| | - Kwan Suk Kim
- Department of Animal Sciences, Chungbuk National University, Chungbuk 28644, Korea
| | | | | | - Pamela Burger
- Research Institute of Wildlife Ecology, Vetmeduni, 1160 Vienna, Austria
| | - Elena Ciani
- Department of Biosciences, Biotechnologies and Biopharmaceutics, University of Bari “Aldo Moro”, 70126 Bari, Italy
| |
Collapse
|
6
|
Auzino B, Miranda G, Henry C, Krupova Z, Martini M, Salari F, Cosenza G, Ciampolini R, Martin P. Top-Down proteomics based on LC-MS combined with cDNA sequencing to characterize multiple proteoforms of Amiata donkey milk proteins. Food Res Int 2022; 160:111611. [DOI: 10.1016/j.foodres.2022.111611] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 06/08/2022] [Accepted: 06/29/2022] [Indexed: 11/24/2022]
|
7
|
Deepika S, Gautam D, Meena S, Ali M, Meena AS, Vats A, Verma M, Rout PK, De S. Heterogeneity and diversified distribution of αS2 casein variants in Indian goats. Small Rumin Res 2021. [DOI: 10.1016/j.smallrumres.2021.106501] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
|
8
|
Miranda G, Bianchi L, Krupova Z, Trossat P, Martin P. An improved LC-MS method to profile molecular diversity and quantify the six main bovine milk proteins, including genetic and splicing variants as well as post-translationally modified isoforms. FOOD CHEMISTRY-X 2020; 5:100080. [PMID: 32123868 PMCID: PMC7037581 DOI: 10.1016/j.fochx.2020.100080] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Revised: 12/08/2019] [Accepted: 02/04/2020] [Indexed: 12/17/2022]
Abstract
Identification of the main milk proteins isoforms are inferred from a mass database. Quantification of the six main milk proteins is achieved from corrected UV at 214 nm. Multiple isoforms can be quantified from mass signal, within each protein family. Glycosylation isoforms of κ-casein which impact micelle stability are quantified.
Here we describe a method based on Liquid Chromatography coupled with Mass Spectrometry (LC-MS) that provides an accurate determination of the six main bovine milk proteins, including allelic and splicing variants, as well as isoforms resulting from post-translational modifications, with an unprecedented level of resolution. Proteins are identified from observed molecular masses in comparison with theoretical masses of intact proteins indexed in an “in-house” database that includes nearly 3000 entries. Quantification was performed either from UV (214 nm) or mass signals. Thus, up to one hundred molecules, derived from the six major milk proteins, can be identified and quantified from an individual milk sample. This powerful and reliable method, initially developed as an anchoring method to estimate the composition of the six main bovine milk proteins from MIR spectra, is transferable to several mammalian species, including small ruminants, camels, equines, rabbits, etc., for which specific mass databases are available.
Collapse
Affiliation(s)
- Guy Miranda
- UMR GABI, INRAE, AgroParisTech, Université Paris-Saclay, 78350 Jouy-en-Josas, France
| | - Leonardo Bianchi
- UMR GABI, INRAE, AgroParisTech, Université Paris-Saclay, 78350 Jouy-en-Josas, France
| | - Zuzana Krupova
- UMR GABI, INRAE, AgroParisTech, Université Paris-Saclay, 78350 Jouy-en-Josas, France
| | | | - Patrice Martin
- UMR GABI, INRAE, AgroParisTech, Université Paris-Saclay, 78350 Jouy-en-Josas, France
| |
Collapse
|
9
|
Application of in silico approaches for the generation of milk protein-derived bioactive peptides. J Funct Foods 2020. [DOI: 10.1016/j.jff.2019.103636] [Citation(s) in RCA: 63] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
|
10
|
Pauciullo A, Shuiep ET, Ogah MD, Cosenza G, Di Stasio L, Erhardt G. Casein Gene Cluster in Camelids: Comparative Genome Analysis and New Findings on Haplotype Variability and Physical Mapping. Front Genet 2019; 10:748. [PMID: 31555318 PMCID: PMC6726744 DOI: 10.3389/fgene.2019.00748] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2018] [Accepted: 07/17/2019] [Indexed: 12/22/2022] Open
Abstract
The structure of casein genes has been fully understood in llamas, whereas in other camelids, this information is still incomplete. In fact, structure and polymorphisms have been identified in three (CSN1S1, αs1-CN; CSN2, β-CN; CSN3, κ-CN) out of four casein genes, whereas controversial information is available for the CSN1S2 (αs2-CN) in terms of structure and genetic diversity. Data from the genome analysis, whose assembly is available for feral camel, Bactrian, dromedary, and alpaca, can contribute to a better knowledge. However, a majority of the scaffolds available in GenBank are still unplaced, and the comparative annotation is often inaccurate or lacking.Therefore, the aims of this study are 1) to perform a comparative genome analysis and synthesize the literature data on camelids casein cluster; 2) to analyze the casein variability in two dromedary populations (Sudanese and Nigerian) using polymorphisms at CSN1S1 (c.150G > T), CSN2 (g.2126A > G), and CSN3 (g.1029T > C); and 3) to physically map the casein cluster in alpaca. Exon structures, gene and intergenic distances, large insertion/deletion events, SNPs, and microsatellites were annotated. In all camelids, the CSN1S2 consists of 17 exons, confirming the structure of llama CSN1S2 gene. The comparative analysis of the complete casein cluster (∼190kb) shows 12,818 polymorphisms. The most polymorphic gene is the CSN1S1 (99 SNPs in Bactrian vs. 248 in dromedary vs. 626 in alpaca). The less polymorphic is the CSN3 in the Bactrian (22 SNPs) and alpaca (301 SNPs), whereas it is the CSN1S2 in dromedary (79 SNPs). In the two investigated dromedary populations, the allele frequencies for the three markers are slightly different: the allele C at CSN1S1 is very rare in Nigerian (0.054) and Sudanese dromedaries (0.094), whereas the frequency of the allele G at CSN2 is almost inverted. Haplotype analysis evidenced GAC as the most frequent (0.288) and TGC as the rarest (0.005). The analysis of R-banding metaphases hybridized with specific probes mapped the casein genes on chromosome 2q21 in alpaca. These data deepen the information on the structure of the casein cluster in camelids and add knowledge on the cytogenetic map and haplotype variability.
Collapse
Affiliation(s)
- Alfredo Pauciullo
- Department of Agricultural, Forest and Food Sciences, University of Torino, Grugliasco, Italy
| | - El Tahir Shuiep
- Institute of Molecular Biology, University of Nyala, Nyala, Sudan
| | - Moses Danlami Ogah
- Department of Animal Science, Nasarawa State University, Keffi, Shabu-Lafia, Nigeria
| | - Gianfranco Cosenza
- Department of Agriculture, University of Napoli Federico II, Portici Italy
| | - Liliana Di Stasio
- Department of Agricultural, Forest and Food Sciences, University of Torino, Grugliasco, Italy
| | - Georg Erhardt
- Department for Animal Breeding and Genetics, Justus Liebig University, Gießen, Germany
| |
Collapse
|