With antibiotic-resistant bacteria on the rise, scientists have been searching for ways to shut down the Type IV secretion system (T4SS), a protein complex on the outer envelope of bacterial cells that helps them to exchange DNA with neighboring bacteria and resist antibiotics.

Now a collaboration between University of Texas Southwestern computational biologist Qian Cong, Ph.D., and molecular biologists at the University of London has elucidated the structure of the T4SS complex, providing a blueprint that could help researchers design drugs that slow the development of antibiotic resistance. 

“For the first time, we determined the 3D structure of the entire T4SS complex,” said Dr. Cong, Assistant Professor of Biophysics in the Eugene McDermott Center for Human Growth and Development at UTSW.

The team in London was led by Gabriel Waksman, Ph.D., whose lab has been working for more than two decades to understand T4SS, especially how it forms a thin, hollow structure called a pilus, which connects to nearby bacteria to share genes. For this project, his team used cryo-electron microscopy (cryo-EM) – a process that freezes proteins and uses beams of electrons to obtain high-resolution microscopic images – to elucidate the structure of T4SS. This was no small feat since the T4SS complex is larger than 99.6% of all those included to date in the worldwide library of protein structures.

Dr. Cong then used her background in statistics and machine learning to analyze T4SS protein sequences from several bacteria to generate structural predictions, which were compared to the cryo-EM data. Her computational analysis supported the cryo-EM data and suggested a hypothesis about the function of T4SS. While it was already known that T4SS is involved in pilus assembly, she predicted how it occurs. With that prediction in hand, Dr. Waksman’s team was able to make specific mutations within the relevant pieces of the complex and validate Dr. Cong’s hypothesis in live bacteria.

“In addition to the contribution we have made toward the development of drugs to slow the spread of antibiotic resistance genes, this study showcases the power of modern computational methods to validate experimental results and suggest functional insights beyond available experimental data,” said Dr. Cong, a Southwestern Medical Foundation Scholar in Biomedical Research.

Other researchers who contributed to this study include Kévin Macé, Abhinav K. Vadakkepat, Adam Redzej, Natalya Lukoyanova, Clasien Oomen, Nathalie Braun, Marta Ukleja, Fang Lu, Tiago R. D. Costa, and Elena V. Orlova of the Institute of Structural and Molecular Biology, Birkbeck College, University of London; and David Baker of the University of Washington.

Scientists from the Department of Traumatology and Acute Critical Medicine at the Osaka University Graduate School of Medicine developed an AI algorithm to predict the risk of mortality for patients suffering a major injury. Using the Japan Trauma Data Bank for the years 2013 to 2017, they were able to obtain records for over 70,000 patients who had experienced blunt-force trauma, which allowed the researchers to identify critical factors that could guide treatment strategies more precisely. 

Trauma doctors in emergency rooms must make life-and-death decisions quickly, and often with very limited information. Part of the challenge is that the factors that would indicate the likelihood of adverse clinical outcomes are not completely understood, and sometimes the body’s inflammatory and blood clotting changes in response to major injuries do more harm than good. A more rigorous and comprehensive approach to trauma care is clearly needed.

Now, a team of researchers from the Osaka University Graduate School of Medicine have analyzed a database of all trauma cases recorded in Japan using machine learning algorithms. This included patient information, such as age and type of injury. In addition, mass spectrometry and proteome analysis were performed on serum from trauma patients at the hospital in Osaka. This provided more specific information on blood markers that could indicate an increase or decrease of specific proteins. “Our study has important clinical implications. It can help identify the patients at highest risk who may benefit most from early intervention,” says first author Jotaro Tachino.

The team used a hierarchical clustering analysis on the data and found that 11 variables were most correlated with an increased mortality rate, which included the type and severity of the injury. In addition, they saw that patients at highest risk often exhibited excessive inflammation or even an acute inflammatory response. They also found protein markers that signaled downregulated coagulation strongly associated with negative outcomes.

“The method that we used for this project can also be extended to the development of new treatment strategies and therapeutic agents for other medical conditions for which large datasets are available,” says senior author Hiroshi Ogura. This work may greatly optimize the allocation of scarce ER healthcare resources to save more people. The team also hopes that this research might help shed light on ways to help calm the inflammation pathways that can run out of control in the wake of traumatic injuries.