Bioinformatics-based Obesity Gene Screening Service

Characteristics of Obesity

Obesity is marked by increased adipose tissue mass and the persistent activation of inflammatory pathways in adipocytes and macrophages. Thus, understanding the genetic and molecular mechanisms behind obesity is crucial for effective intervention. Genes like UCP2, UCP3, SLC6A14, and JHDM2A are known to be involved. Advances in molecular technology and bioinformatics have enabled the screening of more highly differentially expressed genes. At Protheragen, we offer top-tier screening services for Candidate Obesity Genes, providing insights into obesity mechanisms and developing strategic methods to combat it.

Unlock Genetic Insights Through Bioinformatics to Combat Obesity Effectively!

The bioinformatics-based obesity genetic screening service provides an innovative approach to understanding the genetic factors that contribute to obesity. The service uses advanced bioinformatics tools and technologies to identify and analyze genes associated with obesity, giving researchers a deeper understanding of the genetic abnormalities of this disease. The service includes comprehensive genomic analysis, integration of large-scale data sets, and application of complex algorithms to pinpoint key genetic markers. In addition, it provides a detailed workflow involving data collection, DNA sequencing, data analysis, and result interpretation. Our services are as follows:

• Data Collection

  Search for gene expression datasets related to obesity to compare the expression patterns between lean and obese individuals. This analysis provides insights into the molecular mechanisms associated with obesity, helping clients understand the gene expression changes and molecular differences between these groups.

• Identification of Differentially Expressed Genes (DEGs)

  Identifying obesity-related DEGs involves comparing gene expression profiles between obese and non-obese individuals or experimental models. Collected data are background corrected and quantile data normalized to remove low-quality reads and mapped to the reference genome. Differential expression analysis is then performed using bioinformatics tools such as edgeR. These tools use statistical methods to determine which genes show significant differences in expression levels, indicating their potential role in obesity. Identifying DEGs helps researchers understand the molecular pathways and biological processes affected by obesity, thereby identifying potential targets for therapeutic intervention.

• Enrichment Analysis of DEGs

  Once obesity-associated DEGs are identified, enrichment analysis is performed to discern their biological significance. This process often employs tools like Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Enrichment analysis statistically evaluates whether specific biological pathways, processes, or molecular functions are overrepresented in the identified DEGs.

• Construction and Analysis of Protein-protein Interaction (PPI) Networks

  We integrate the identified DEGs with known protein interaction data to map the interactions between the proteins encoded by these genes. The Search Tool for Retrieval of Interacting Genes (STRING) database is used to construct these networks. Once constructed, these PPI networks are analyzed to identify key proteins (hubs) that play important roles in the networks, thereby discovering key interaction points that may be critical for understanding the molecular mechanisms of obesity. This network analysis helps identify potential biomarkers and therapeutic targets by highlighting essential proteins and their interactions in biological systems affected by obesity.

• Construction of Target Genes—miRNA Regulatory Networks and Transcription Factors (TFs) Regulatory Networks

  This construction process involves mapping the regulatory relationships between target genes and their upstream regulators, such as miRNAs and TFs. The miRNA regulatory network illustrates miRNAs that affect gene expression post-transcriptionally, while the TF regulatory network highlights transcription factors that bind to gene promoters to regulate transcription. Integrating these networks provides insights into the multi-level regulation of gene expression in obesity, revealing new therapeutic targets and biomarkers.

• Verification of Hub Genes

  After identifying hub genes through network analysis, we use various experimental techniques to validate their importance. Gene expression analysis using quantitative reverse transcription-polymerase chain reaction (qRT-PCR) or RNA sequencing confirms differential expression levels. Functional analysis, including gene knockdown or overexpression studies, helps determine the impact of hub genes on cellular processes. Co-immunoprecipitation (Co-IP) and mass spectrometry validate physical interactions with other proteins. In addition, phenotypic analysis in model organisms or cell lines provides insights into the biological functions and relevance of these hub genes in obesity, solidifying their potential as therapeutic targets.

• Anti-Obesity Therapy Development

  After identifying hub genes, we develop anti-obesity gene therapy targeting specific genes. We also develop other anti-obesity therapies targeting specific targets to meet the needs of our clients.

  

  Small Molecule Drug

  

  Gene Therapy

  

  Cell Therapy

  

  Antibody Therapy

  

  Tissue Engineering Therapy

  

  Nanotherapy

• Preclinical Studies of Anti-Obesity Therapeutics

  Preclinical research on anti-obesity therapies is a key step in developing effective obesity therapies, involving a large number of cell studies, animal studies, etc. In vivo obesity models (such as obese transgenic mice) are often used to simulate human metabolic disorders to evaluate the pharmacokinetics, pharmacodynamics, and potential side effects of new therapies. In vitro cell models are also used to evaluate the effectiveness of new therapies.

  Pharmacodynamic Study

  Pharmacokinetic Study

  Bioanalysis

  Safety Assessment

Workflow

Applications

• Identify specific genetic markers associated with increased risk of obesity, and more accurately predict individual susceptibility to obesity by analyzing individual genetic variations, thereby developing early intervention and prevention strategies.
• Study obesity-related DEGs to gain a deeper understanding of the molecular pathways and biological processes that lead to this complex disease. This understanding is critical to uncovering the complex mechanisms that lead to obesity, thereby laying the foundation for effective treatment.
• Bioinformatics tools help to pinpoint obesity-related genes, thereby designing drugs and treatments that specifically target the molecular drivers of obesity, improving treatment efficacy and minimizing side effects.

Advantages

• High precision: We use advanced sequencing technology and databases to ensure that the results are highly accurate and reliable, minimizing the risk of false positives and improving the overall quality of the data.
• Comprehensive data analysis: We use sophisticated algorithms and software to identify complex patterns and interactions between genes, providing a detailed understanding of the genetic factors that contribute to obesity.
• Personalized insights: We provide tailored insights based on an individual's unique genetic makeup. By analyzing a person's specific genetic profile, customized recommendations and interventions are provided, resulting in more effective and personalized obesity management strategies.
• Expert team: Supported by an experienced team of bioinformaticians and geneticists with extensive knowledge and expertise in the field. Their combined experience ensures that every aspect, from data collection and analysis to interpretation of results, is performed at the highest professional level.

Publication Data

Frequently Asked Questions

Why is it important to screen for obesity-related genes?

Obesity is multifactorial and involves a complex interaction between genetic, environmental, and lifestyle factors, so screening for obesity-related genes is crucial. Identifying genetic predispositions provides insights into the molecular mechanisms of obesity and helps to gain a deeper understanding of its pathogenesis. In addition, identifying specific genetic markers helps develop targeted treatment strategies and improve treatment outcomes.

What bioinformatics tools are used for data analysis?

We use gene expression analysis tools to identify differentially expressed genes, PPI network tools to construct and visualize protein-protein interactions STRING, and construct miRNA and TF regulatory networks.

Protheragen uses advanced sequencing technologies and comprehensive computational analysis to identify key genetic markers that contribute to obesity. Our tailored insights provide personalized strategies for the development of obesity-related therapeutics, aiming to mitigate the risk of obesity and its related diseases. Please feel free to contact us to transform genetic data into actionable healthcare solutions for a healthier future.

All of our services and products are intended for preclinical research use only and cannot be used to diagnose, treat or manage patients.