Liman Bio focuses on the research and development of high-throughput gene editing tools

Serving new drug development and basic research

Background

Genomics-based precision medicine research has achieved initial success, but the complexity of gene transcription and translation processes hinders the identification of causal genes, thus limiting the development of precision medicine. Proteins can directly reflect the body's biological processes and pathological changes, and are key to understanding disease mechanisms and developing new therapies.

 

In recent years, high-throughput proteomics Technological advances have provided an excellent opportunity to systematically study the protein profiles of health and disease, helping to understand mechanisms, biomarker identification, risk prediction, early detection of adverse drug reactions, and aging. Despite this, most proteomic studies currently focus only on limited disease outcomes, many diseases have not been fully explored, and there is a lack of a comprehensive map of human proteome and phenome associations.

 

The Liman team focuses on high-throughput gene editing technology Research and development, and is gradually integrating with high-throughput proteomics technology Deep integration has become a routine technical means in the fields of perturbation proteomics research, large-scale biomarker verification, and screening and verification of potential drug targets, playing an indispensable role in basic research, early disease screening, and drug target discovery.

 

I. Proteomics research provides a new perspective for precision medicine

Recently, Professors Jintai Yu, Ying Mao, Wei Cheng, and Jianfeng Feng of Fudan University jointly corresponded and published a research paper entitled "Atlas of the Plasma Proteome in Health and Disease in 53,026 Adults" online in the journal Cell. This paper comprehensively maps the human proteome in health and disease, revealing the expression changes of plasma proteins in health and various disease states, providing valuable information for early diagnosis of diseases, risk prediction, and the discovery of potential therapeutic targets.

 

This study, based on samples from 53,026 adults (median follow-up 14.8 years) from the UK Biobank, mapped the most comprehensive protein map to date globally covering 2920 plasma proteins, with 406 previously diagnosed diseases, 660 newly diagnosed diseases during follow-up, and 986 health-related characteristics.

This protein map shows 168,100 associations between proteins and diseases, and 554,488 relationships between proteins and various health traits The study revealed associations between at least 50 diseases and more than 650 proteins, as well as heterogeneity in more than 1000 proteins across sex and age. Particularly noteworthy is the finding that proteins have significant potential in disease identification, with an area under the curve (AUC) exceeding 0.80 for 183 diseases, demonstrating excellent predictive ability.

By further integrating protein quantitative trait locus (pQTL) and genome-wide association study (GWAS) data, Mendelian randomization analysis (MR) identified 474 protein-disease pairs with potential causal associations, including 18 proteins related to neuropsychiatric diseases. 37 targets with potential for drug repurposing were also identified. In addition, 26 proteins were shown to have good performance in drug safety providing new directions for future drug development and treatment.

 

II. Open resources to support future research

This study not only provides a new perspective on proteomics, but also creates an open-access integrated protein-phenotype resource platform (https://proteome-phenome-atlas.com/), providing valuable data resources for scientists worldwide to help deepen understanding of disease biological mechanisms and accelerate the development of disease biomarkers, predictive models, and therapeutic targets.

In the future, the combination of proteomics and machine learning technologies will further advance the study of health and disease states. By mining large-scale proteomic data and combining advanced machine learning models, it will be possible to more effectively identify biomarkers and predictive models for various diseases, providing a reliable basis for early diagnosis and personalized treatment.

 

III. Conclusion

With the rapid growth and aging of the global population, the demand for disease prevention and treatment is constantly increasing. The advancement of precision medicine requires comprehensive support from basic research to clinical applications, and proteomics undoubtedly provides powerful tools and data support for this process. With the continuous development of this technology, we have reason to believe that future medicine will be more precise and efficient, and bring more hope for individualized treatment.

 

The Liman team is targeting the 2920 plasma proteins mentioned in this article, and has completed the above genes in human/mouse-derived cell lines in vitro models KO knockout cell lines are available Construction, welcome relevant research teams to contact us! The Liman team hopes to accelerate and assist your product development and basic research!

 

Literature Source

https://doi.org/10.1016/j.cell.2024.10.045 (Download the original article by leaving a message on the official account)