Mitochondrial transfer: Impact on cancer and immunity

Mitochondria, those cellular powerhouses, are at the heart of metabolic activities. Recent research reveals a one-sided transfer of mitochondria from T cells to cancer cells, a process that strengthens cancer cells while weakening the immune cells.

This article delves into this intriguing exchange, empowered by single-cell RNA sequencing and MERCI, a novel statistical tool for tracking mitochondrial movement. Validation shows MERCI’s precision in predicting recipient cells and their mitochondrial compositions.

Applying MERCI to human cancer samples uncovers a reproducible mitochondrial transfer pattern with signature genes linked to critical processes. Furthermore, this phenomenon correlates with increased cell cycle activity and adverse clinical outcomes across various cancer types. Read more about this under Article 2 below.

Article 1: Probiotic-guided CAR-T cells for solid tumor targeting

Researchers have developed a new way to make cancer treatment more effective, especially for solid tumors. They created special bacteria that can go to these tumors and help immune cells fight the cancer. They first modified the bacteria to carry markers to the tumor and then modified immune cells to recognize these markers. When they put the bacteria in the body, the immune cells followed them to the tumor and attacked the cancer cells. This method worked well in experiments with breast and colon cancer. Read the full article here.

In summary: Engineered bacteria to improve solid tumor cancer treatment

Article 2: Systematic investigation of mitochondrial transfer between cancer cells and T cells at single-cell resolution

Mitochondria are like powerhouses in our cells, responsible for energy production. Researchers recently discovered that cancer cells can take mitochondria from T cells, weakening the immune cells while strengthening the cancer cells. To understand this process better, scientists developed a method called MERCI, which helps trace and measure the movement of mitochondria between cancer and T cells using genetic information. They used MERCI to study samples from human cancer patients and found a consistent pattern of mitochondrial transfer, involving genes related to energy production and cell signaling. This transfer was linked to increased cell activity and worse patient outcomes in different types of cancer. They concluded that MERCI helps to better understand how cancer cells take mitochondria from immune cells, potentially leading to new treatments. Read the full article here.

In summary: MERCI method reveals cancer cells taking mitochondria from immune cells

Article 3: Functional filter for whole-genome sequencing data identifies HHT and stress-associated non-coding SMAD4 polyadenylation site variants >5 kb from coding DNA

Despite advanced genetic sequencing, many single-gene disorders remain unsolved. A new tool, GROFFFY, was developed to tackle this issue. It focuses on non-coding DNA regions rich in regulatory sequences. In hereditary hemorrhagic telangiectasia (HHT) cases, GROFFFY significantly reduced potential disease-causing variants without missing relevant ones. In three unsolved HHT cases, GROFFFY identified rare deletions in a non-coding region of the SMAD4 gene, affecting its ability to produce a crucial protein. This discovery uncovers a rare type of genetic variant impacting vital regulatory systems. GROFFFY is a valuable tool for identifying disease-causing genetic variants beyond protein-coding regions, potentially aiding more individuals with genetic disorders. Read the full article here.

In summary: GROFFFY identifies non-coding genetic variants, aiding diagnosis of rare disorders

Article 4: Age-dependent topic modeling of comorbidities in UK Biobank identifies disease subtypes with differential genetic risk

Researchers are using electronic health records to better understand and personalize medical care. They’ve developed a method called age-dependent topic modeling (ATM) to analyze data from a large group of people (over 282,000 in the UK Biobank). Using this method, they found 52 different diseases that have unique patterns of being diagnosed together (comorbidity). By studying these patterns, they were able to identify subtypes of these 52 diseases and understand how genetics plays a role in each subtype. In total, they found 18 disease subtypes with different genetic risk profiles, meaning that specific genetic factors were linked to different ways the diseases presented in patients. The ATM method helps researchers find different disease subtypes and understand how genetics affects each subtype, potentially leading to more personalized medical care. Read the full article here.

In summary: Method to identify 18 disease subtypes with distinct genetics

References

[1] Vincent, R. L., Gurbatri, C. R., Li, F., Vardoshvili, A., Coker, C., Im, J., Ballister, E. R., Rouanne, M., Savage, T., de Los Santos-Alexis, K., Redenti, A., Brockmann, L., Komaranchath, M., Arpaia, N., & Danino, T. (2023). Probiotic-guided CAR-T cells for solid tumor targeting. Science (New York, N.Y.), 382(6667), 211–218. https://doi.org/10.1126/science.add7034

[2] Zhang, H., Yu, X., Ye, J., Li, H., Hu, J., Tan, Y., Fang, Y., Akbay, E., Yu, F., Weng, C., Sankaran, V. G., Bachoo, R. M., Maher, E., Minna, J., Zhang, A., & Li, B. (2023). Systematic investigation of mitochondrial transfer between cancer cells and T cells at single-cell resolution. Cancer cell, 41(10), 1788–1802.e10. https://doi.org/10.1016/j.ccell.2023.09.003

[3] Xiao, S., Kai, Z., Murphy, D., Li, D., Patel, D., Bielowka, A. M., Bernabeu-Herrero, M. E., Abdulmogith, A., Mumford, A. D., Westbury, S. K., Aldred, M. A., Vargesson, N., Caulfield, M. J., Genomics England Research Consortium, & Shovlin, C. L. (2023). Functional filter for whole-genome sequencing data identifies HHT and stress-associated non-coding SMAD4 polyadenylation site variants >5 kb from coding DNA. American journal of human genetics, S0002-9297(23)00318-X. Advance online publication. https://doi.org/10.1016/j.ajhg.2023.09.005

[4] Jiang, X., Zhang, M. J., Zhang, Y., Durvasula, A., Inouye, M., Holmes, C., Price, A. L., & McVean, G. (2023). Age-dependent topic modeling of comorbidities in UK Biobank identifies disease subtypes with differential genetic risk. Nature genetics, 10.1038/s41588-023-01522-8. Advance online publication. https://doi.org/10.1038/s41588-023-01522-8