Researchers from St. Jude Children’s Research Hospital and the Medical College of Wisconsin have made a groundbreaking discovery that sheds light on how cells travel through the body. By developing a data science framework, they were able to analyze chemokines and their associated G protein-coupled receptors (GPCRs), which are proteins that govern cell movement.

The scientists found that specific positions within structured and disordered regions of both proteins determine how chemokines and GPCRs bind each other. This understanding enabled them to artificially change chemokine-GPCR binding preferences and alter the resulting cell migration. Their findings have significant implications for disease treatment, such as enhancing cellular therapies’ ability to reach tumor sites, and increasing clarity about healthy processes like heart and blood vessel development.

Cell migration is a crucial process that influences many aspects of our bodies, including how immune cells travel to infection sites, brain development, and wound repair. However, the vast similarities between members of each protein family have presented a challenge in understanding how correct pairs form and control cell movement. The researchers’ data-driven approach identified the exact parts of each protein governing their molecular interactions.

“We found that cells have an elegant system that uses structure and disorder together to control cell migration,” said senior co-corresponding author M. Madan Babu, PhD. “With this understanding, we can now rationally introduce small changes in a chemokine’s structure to ultimately alter cell migration in desired ways.”

The scientists compared all human chemokine-binding GPCRs and all chemokines, then compared similar chemokines and GPCRs from other species. They also looked at each protein individually at a population level, finding places that stayed the same across groups and those that differed.

“Through our data analysis, we discovered that the information for how chemokines and GPCRs select for each other is stored in small, discrete packages of highly unstructured, disordered regions,” said first and co-corresponding author Andrew Kleist, MD. “The mix of those small packages from both the chemokine and receptor results in the unique interaction, similar to website data encryption keys, which governs cell migration.”

This discovery has significant implications for disease treatment and therapy development. The researchers’ framework can guide exploration into new medicines and improvements for existing cellular therapies.

“Now that we’ve shown a proof of concept, our approach will guide exploration into new medicines and improvements for existing cellular therapies,” Kleist said. “For example, it may be possible to create molecules that better lead immune cells to cancers or help recruit more blood stem cells for bone marrow transplants.”

The framework is freely available online at: https://github.com/andrewbkleist/chemokine_gpcr_encoding.

When people think about the body, we often think every cell stays in place. However, that’s a simplistic view. Depending on the tissue, cells are moving all the time, and our new understanding of those systems opens novel avenues for therapeutic development.

This discovery has the potential to revolutionize our understanding of cell movement and its role in various biological processes. By unlocking the code of cell movement, researchers can develop more effective treatments and therapies that target specific aspects of cellular behavior.

The article “Tailoring Gene Editing with Machine Learning: A Breakthrough in CRISPR-Cas9 Enzyme Engineering” discusses how researchers from Mass General Brigham have harnessed machine learning to revolutionize the field of genome editing. By developing a machine learning algorithm called PAMmla, they’ve predicted the properties of over 64 million genome editing enzymes, significantly expanding our repertoire of effective and safe CRISPR-Cas9 enzymes.

CRISPR-Cas9 enzymes are powerful tools for editing genes, but their traditional application can have off-target effects, modifying DNA at unintended sites in the genome. The researchers’ novel approach uses machine learning to better predict and tailor these enzymes, ensuring greater specificity and accuracy in gene editing. This scalable solution has the potential to transform our understanding of genetic conditions and unlock new therapeutic targets.

The study showcases the power of PAMmla by demonstrating its utility in precise editing disease-causing sequences in primary human cells and mice. The researchers have also made a web tool available for others to use this model, enabling the community to create customized enzymes tailored for specific research and therapeutic applications.

Ben Kleinstiver, PhD, and Rachel A. Silverstein, PhD candidate, are leading authors on this study, highlighting the potential of machine learning in expanding our capabilities in gene editing. This breakthrough has significant implications for the field, offering a new era of precision and safety in genome editing technology.

The proliferation of antibiotic resistance genes (ARGs) poses a significant threat to public health worldwide. To combat this issue, understanding how ARGs are transmitted and implemented preventive measures is crucial. A research team led by Professor Tong Zhang from the University of Hong Kong has developed an innovative computational tool called Argo that tracks ARGs in environmental samples, providing valuable insights into their dissemination.

The current high-throughput DNA sequencing technique used for tracking ARGs often fails to provide information on the hosts carrying these genes. This limits our ability to accurately assess the risks associated with ARGs and trace their transmission, hindering our understanding of their impact on human health and the environment.

Argo utilizes long-read sequencing, a method that generates significantly longer DNA fragments than traditional short-read sequencing techniques. By assigning taxonomic labels to read clusters (collections of reads that overlap each other), Argo enhances the detection resolution of ARGs. The key difference between Argo and existing tools lies in its method of grouping and analyzing DNA fragments based on their overlaps, rather than individual reads.

The accuracy of host identification is a significant advantage of Argo over existing tools. By solving a “puzzle” using shared features among DNA fragment pieces, researchers can group and label overlapping or similar pieces more effectively. Simulations have shown that Argo achieves the lowest misclassification rate compared to other tools.

While long-read sequencing remains costly for achieving high throughput, the team believes this new method is vital in addressing the growing threat posed by ARGs. Professor Zhang concludes that “Argo has the potential to standardize ARGs surveillance and enhance our ability to trace the origins and dissemination pathways of ARGs, contributing to efforts to tackle the global health threat of antimicrobial resistance (AMR).”