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    The Benefits of Professional Assignment Help Services

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    The bovine genome, comprising the entire genetic blueprint of cattle, has been a subject of extensive research and exploration. In recent years, breakthroughs in genomics have enabled scientists to delve deep into the genetic makeup of bovines, leading to remarkable advancements in understanding bovine traits, parentage verification, and harnessing genetic potential for improved breeding programs. This article aims to shed light on the significance of the bovine genome, the role of Single Nucleotide Polymorphisms (SNPs), and the application of bovine parentage assessment.

    The Bovine Genome: A Window into Bovine Biology


    The bovine genome represents the complete set of DNA present in cattle, providing insights into the inheritance of various physical and physiological traits. Decoding the bovine genome has been a monumental undertaking, with the sequencing efforts of the Bovine Genome Project playing a pivotal role. These efforts, involving multiple genome assemblies, gene predictions, and annotation, have laid the foundation for understanding the genomic composition of bovines.

    Overview of Drung cattle (Bos frontalis) reference genome.

    Overview of Drung cattle (Bos frontalis) reference genome. (Chen et al., 2022)

    Unlocking Genetic Variation with SNPs


    Single Nucleotide Polymorphisms, or SNPs, are minute variations in a single DNA building block, or nucleotide, within the genome. SNPs act as genetic markers that can be used to identify genetic differences between individuals. In the context of bovines, the sequencing of multiple breeds has facilitated the identification of a panel of SNPs specific to cattle. These SNPs serve as powerful tools for genetic studies, enabling researchers to investigate traits, heritability, and genetic variations across different breeds.

    Bovine Parentage Assessment: Ensuring Genetic Lineage


    Bovine parentage assessment plays a vital role in verifying the genetic parentage of cattle, ensuring accurate pedigrees and maintaining the integrity of breeding programs. By utilizing DNA profiling and comparing genetic markers, scientists can determine the true parentage of individual bovines. The process involves collecting DNA samples from potential parents and offspring, followed by DNA extraction and analysis using specialized laboratory techniques. Statistical analysis is then employed to assess the probability of parentage based on shared genetic markers.

    How To Do Bovine Parentage Assessment?


    Bovine parentage verification is a crucial aspect of livestock management, breeding programs, and genetic improvement in cattle. Traditional methods for establishing bovine parentage relied on microsatellite markers, which provided accurate results but were labor-intensive, time-consuming, and limited in their scope. However, recent advancements in agrigenomics have revolutionized bovine parentage assessment by leveraging high-throughput sequencing and single nucleotide polymorphism (SNP) genotyping technologies. This article explores the application of bovine parentage sequencing panels and SNP-based genotyping in the context of bovine parentage verification and its implications for the industry.

    Bovine Parentage Sequencing Panels

    Bovine parentage sequencing panels encompass a set of targeted sequencing assays that capture genetic information relevant to parentage verification. These panels are designed to efficiently genotype specific regions of the bovine genome associated with parentage determination. By utilizing massively parallel sequencing platforms, a large number of animals can be genotyped simultaneously, resulting in a high-throughput and cost-effective approach to bovine parentage assessment.

    SNP-based Genotyping

    Single nucleotide polymorphisms (SNPs) are the most abundant form of genetic variation in the bovine genome. SNPs are single base pair changes and occur at a frequency of approximately one in every 100-300 nucleotides. SNP-based genotyping platforms allow for the simultaneous detection of thousands to millions of SNPs across the genome. These genotyping arrays or sequencing-based methods provide a comprehensive view of an animal's genetic makeup, including parentage information.

    Workflow of Bovine Parentage Assessment


    Bovine parentage analysis helps establish accurate pedigrees, verify breeding records, and ensure the integrity of breeding programs and genetic improvement efforts within the cattle industry.

    The process of bovine parentage assessment typically involves the following steps:

    • Sample collection: DNA samples are collected from the animals whose parentage needs to be verified. Usually, samples are obtained by using special ear tags or blood sampling kits that facilitate DNA collection.
    • DNA extraction: The collected samples, such as ear tissue or blood, are processed to extract DNA. This is typically performed using laboratory techniques to isolate the DNA from the cellular material in the sample.
    • DNA profiling: The extracted DNA is then subjected to DNA profiling, which involves analyzing specific genetic markers called microsatellites or single nucleotide polymorphisms (SNPs). These markers are highly variable and inherited from both parents, making them ideal for parentage testing.
    • Comparison and analysis: The DNA profiles of the potential parents and the offspring are compared to identify the presence or absence of shared genetic markers. If the genetic markers of the offspring are consistent with those of the putative parents, the parentage can be confirmed.
    • Statistical analysis: The comparison results are statistically evaluated to determine the likelihood of parentage based on the genetic information. Software programs are often used to calculate the probability of parentage and generate a report.

    Advantages of Bovine Parentage Sequencing and SNP Genotyping


    Accuracy and Reliability

    Bovine parentage sequencing and SNP genotyping offer higher accuracy and reliability compared to traditional methods. Microsatellite markers, prone to genotyping errors due to allele dropout and stuttering, are replaced by SNP markers that provide unambiguous genotypes, reducing the chances of misassignment or false exclusions.

    Efficiency and Scalability

    The high-throughput nature of bovine parentage sequencing panels and SNP genotyping platforms enables rapid processing of large numbers of samples. This scalability allows for more comprehensive and frequent parentage assessments, facilitating improved breeding decisions, monitoring of genetic diversity, and effective management of breeding programs.

    Genetic Diversity and Genomic Selection

    Bovine parentage sequencing and SNP genotyping data contribute to the expansion of genetic databases, enhancing our understanding of bovine genetic diversity. Additionally, these data can be integrated into genomic selection models, allowing for more accurate prediction of genetic merit and selection of superior animals.

    The Future Perspectives of Bovine Parentage Sequencing


    The integration of bovine parentage sequencing panels and SNP genotyping has already revolutionized bovine parentage assessment. However, continuous research and development are essential to unleash the full potential of these technologies.

    Enhanced Panel Designs

    Further refinement of bovine parentage sequencing panels is crucial to improve the coverage of genomic regions associated with parentage determination. Optimizing panel design to include informative SNPs in diverse populations will ensure accurate parentage assessment across different breeds and populations.

    Bioinformatics and Data Analysis

    As the volume of sequencing and genotyping data increases, advanced bioinformatics tools and algorithms must be developed to efficiently process and analyze these vast datasets. Implementing robust pipelines for variant calling, quality control, and parentage inference will enhance the accuracy and reliability of results.

    Integration with Genomic Technologies

    Integrating bovine parentage sequencing and SNP genotyping data with other genomic technologies, such as whole-genome sequencing and functional genomics, can provide a comprehensive understanding of the genetic architecture underlying parentage verification. This integration can enhance our knowledge of complex inheritance patterns and contribute to the development of novel breeding strategies.

    Cost Reduction and Accessibility

    Continued efforts to reduce the cost of sequencing and genotyping technologies will make bovine parentage assessment more accessible to a wider range of stakeholders, including small-scale producers and developing regions. Making these technologies affordable and user-friendly will democratize their benefits and support sustainable livestock production worldwide.

    Ethical and Regulatory Considerations

    With the growing availability and utilization of genomic data in livestock management, it is essential to address ethical considerations related to data privacy, informed consent, and ownership. Robust regulatory frameworks need to be established to protect the rights and interests of all stakeholders involved in bovine parentage assessment.

    Reference

    1. Chen, Yan, et al. "A draft genome of Drung cattle reveals clues to its chromosomal fusion and environmental adaptation." Communications Biology 5.1 (2022): 353.
    Posted in: Acoustic | 0 comments

    Breast cancer is a malignant tumor that occurs in the glandular epithelial tissue of the breast, and is one of the most typical malignant diseases. Breast cancer is usually classified into five subtypes based on immunohistochemical markers, tumor grade, and proliferation level. This classification determines the means of treatment. However, due to cancer recurrence, drug resistance, or cancer metastasis, a large proportion of treatment methods are not successful. Therefore, the current classification system cannot fully capture the genetic and molecular status of cancer, and a refined classification system may be more conducive to predicting appropriate therapy and scope for different patients.

    How Does SWATH-MS Improve Breast Cancer Classification


    In July 2019, Ruedi Aebersold's research team published a research paper titled "Breast Cancer Classification Based on Proteotypes Obtained by SWATH Mass Spectrometry" in Cell Reports. In this study, SWATH-MS (DIA) technology was used to perform large-scale protein quantification of breast cancer tissues, and in-depth investigation of the five common subtypes of breast cancer was conducted. The study found that the consistency between protein subtypes and conventional classification subtypes reached 84%, revealing three key proteins that contributed most to the classification. Differences were also found between traditional tumor classification subtypes and protein level classification subtypes. These findings demonstrate that this technology can provide a refined classification of breast cancer at the protein level, and provide great help for the treatment strategy of breast cancer patients.

    Why Need a Refined Classification System For Breast Cancer

    1. Establishment of a spectral library for quantitative analysis of breast cancer-associated proteins by SWATH-MS


    The authors established a comprehensive breast cancer proteome spectral library by analyzing samples from breast cancer patients. The reference library contained 28,233 peptides and their modified variants ([FDR]<0.01), representing 4,443 proteins. This spectral library was used for quantitative analysis of proteins in breast cancer tissues identified by SWATH-MS (DIA) in subsequent experiments.

    2. SWATH-MS analysis quantifies 2,842 proteins in 96 patient samples


    The authors conducted proteomic analysis on tumor tissues from 96 breast cancer patients, which were classified into five conventional breast cancer subtypes based on their lymph node status, ER, PR, HER2 status, and tumor grade by pathologists. For each subtype, lymph node negative and lymph node positive samples were pooled separately, resulting in 10 sample pools. Using the SWATH (DIA) spectral library established above, the authors extracted quantitative data of 25,278 protein-specific peptides and their modified variants, representing 2,842 proteins in all individual samples. These 2,842 quantified proteins cover most of the known molecular processes related to breast cancer.

    3. The comparison between protein subtypes and conventional breast cancer subtypes


    The authors performed unsupervised clustering analysis on the 96 samples. In Figure 1A, lymph node-negative and -positive samples of each subtype were closely clustered together, indicating high reproducibility of the experiment. In Figure 1B, correlation clustering analysis was performed on the 96 samples, and the authors found that the clustering of protein subtypes was very close to that of traditional tumor subtypes. They also found that some subtypes had higher heterogeneity, indicating that traditional tumor classification may not have considered tumor heterogeneity.

    Study on the correlation between conventional subtypes and protein subtypes classification of breast cancer tissueFigure1 | Study on the correlation between conventional subtypes and protein subtypes classification of breast cancer tissue

    4. Screening and validation of key proteins


    By analyzing the breast cancer-related protein pathways, key proteins for breast cancer subtypes were screened. After dividing the 96 samples into five conventional subtypes, a decision tree was established, which revealed a list of 22 key proteins (with some overlap in different comparisons). These 22 proteins in the decision tree contained three key nodes.

    Classification of breast cancer patients based on tumor tissue protein levels.Figure2 | Classification of breast cancer patients based on tumor tissue protein levels.

    The differential expression of these three proteins reflects the key clinical parameters of breast cancer subtypes: ER status, tumor grade, and HER2 status. Using the decision tree, breast cancer protein subtypes were classified with 84% concordance with traditional classification. Furthermore, statistical analysis of the key proteins in the 96 samples and in 116 samples reported in other studies showed significant differences in all three key node proteins, confirming that protein-level breast cancer subtype classification is more accurate and reliable than traditional classification.

    The expression of ERBB2 protein and transcription in ER-/HER2+ and ER+/HER2+ breast cancer tissuesFigure 3 | The expression of ERBB2 protein and transcription in ER-/HER2+ and ER+/HER2+ breast cancer tissues

    The author conducted a correlation analysis of fold changes at the protein and transcriptome level for 883 breast cancer patients worldwide. First, they performed a correlation analysis of fold changes for 2,782 proteins obtained from SWATH-MS (DIA), and the overall fold changes ranged between R=0.17 to R=0.29. Then, they analyzed the fold changes of the three key proteins identified in the decision tree, which ranged between R=0.67R=0.81. Although the global fold changes of total proteins were relatively low, the fold changes of the key proteins were high, indicating the importance of studying breast cancer at the protein level and classifying breast cancer subtypes based on protein expression.

    Correlation analysis of the protein levelsFigure 4 | Correlation analysis of the protein levels

    Conclusion


    This study explored and confirmed the applicability of SWATH-MS (DIA) for protein subtyping of human tumor samples under relatively high throughput conditions. The results showed that protein-based classification was more effective than traditional classification in resolving breast cancer subtypes and has the potential to improve traditional classification. In addition, it also demonstrated the great potential of data-independent acquisition methods (such as SWATH-MS) for research. This method of tissue classification is not limited to breast cancer and is effective for classifying other diseases and clinical specimens. Although we are not yet at the stage of making clinical decisions based on protein subtyping data, this study can further stimulate the research process and obtain more comprehensive treatments and better clinical outcomes. The SWATH spectrum library of breast cancer and high-quality proteomics data of 96 breast tumors will provide valuable resources for future protein marker research.

    This study investigated the accuracy of SWATH-MS (DIA) for qualitative and quantitative analysis of breast cancer tissue under high-throughput conditions, demonstrating the powerful potential of this method and confirming the feasibility of protein-based classification for breast cancer subtypes, providing new directions for the diagnosis and treatment of breast cancer.

    Reference:

    1. Ruedi Aebersold et al,. Breast Cancer Classification Based on Proteotypes Obtained by SWATH Mass Spectrometry. cell reports 2019
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