New study observes Tuberculosis bacteria attacking antibiotics

Photograph by torange.biz

Tuberculosis was one of the deadliest known diseases, until antibiotics were discovered and used to dramatically reduce its incidence throughout the world. Unfortunately, before the infectious disease could be eradicated, drug-resistant forms emerged as a major public health threat — one quarter of the world’s population is currently infected with TB and 600,000 people develop drug-resistant TB annually.

New research at SLAC National Accelerator Laboratory is seeking to better understand how this antibiotic resistance develops, as recently reported in BMC Biology.

TB is caused by Mycobacterium tuberculosis bacteria, which attack the lungs and then spread to other parts of the body. The bacteria are transmitted to other people through the air, when an infected person speaks, coughs or sneezes.

These bacteria survive antimicrobial drugs by mutating. Their resilience is enhanced by the lengthy and complex nature of standard treatment, which requires patients to take four drugs every day for six to nine months. Patients often don’t complete this full course of TB treatment, causing the bacteria to evolve to survive the antibiotics.

Now, a team of international researchers has investigated an enzyme, called beta-lactamase, that is produced by the Mycobacterium tuberculosis bacteria. They wanted to understand the critical role this enzyme plays in TB drug resistance.

Specifically, the researchers made tiny crystals of beta-lactamase and mixed them with the antibiotic ceftriaxone. A fraction of a second later, they hit the enzyme-antibiotic mixture with ultrafast, intense X-ray pulses from SLAC’s Linac Coherent Light Source — taking millions of X-ray snapshots of the chemical reaction in real time for two seconds.

Putting these snapshots together, the researchers mapped out the 3D structure of the antibiotic as it interacted with the enzyme. They watched the bacterial enzyme bind to the antibiotic and then break open one of its key chemical bonds, making the antibiotic ineffective.

“For structural biologists, this is how we learn exactly how biology functions,” said Mark Hunter, PhD, staff scientist at SLAC and co-author on the study, in a recent news release. “We decipher a molecule’s structure at a certain point in time, and it gives us a better idea of how the molecule works.”

The research team plans to use their method to study additional antibiotics, observing in real time the rapid molecular processes that occur as the bacteria’s enzymes breakdown the drugs. Ultimately, they hope this knowledge can be used to design better antibiotics that can fight off these attacks.

This is a reposting of my Scope blog story, courtesy of Stanford School of Medicine.

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Artificial intelligence could help diagnose tuberculosis in remote regions, study finds

Image courtesy of Paras Laknani

Tuberculosis is an infectious disease that kills almost two million people worldwide each year, even though the disease can be identified on a simple chest X-ray and treated with antibiotics. One major challenge is that TB-prevalent areas typically lack the radiologists needed to screen and diagnose the disease.

New artificial intelligence models may help. Researchers from the Thomas Jefferson University Hospital in Pennsylvania have developed and tested an artificial intelligence model to accurately identify tuberculosis from chest X-rays, such as the TB-positive scan shown at right.

The model could provide a cost-effective way to expand TB diagnosis and treatment in developing nations, said Paras Lakhani, MD, study co-author and TJUH radiologist, in a recent news release.

Lakhani performed the retrospective study with his colleague Baskaran Sundaram, MD, a TJUH cardiothoracic radiologist. They obtained 1007 chest X-rays of patients with and without active TB from publically available datasets. The data were split into three categories: training (685 patients), validation (172 patients) and test (150 patients).

The training dataset was used to teach two artificial intelligence models — AlexNet and GoogLeNet — to analyze the chest X-ray data and classify the patients as having TB or being healthy. These existing deep learning models had already been pre-trained with everyday nonmedical images on ImageNet. Once the models were trained, the validation dataset was used to select the best-performing model and then the test dataset was used to assess its accuracy.

The researchers got the best performance using an ensemble of AlexNet and GoogLeNet that statistically combined the probability scores for both artificial intelligence models — with a net accuracy of 96 percent.

The authors explain that the workflow of combining artificial intelligence and human diagnosis could work well in TB-prevalent regions, where an automated method could interpret most cases and only the ambiguous cases would be sent to a radiologist.

The researchers plan to further improve their artificial intelligence models with more training cases and other artificial intelligence algorithms, and then they hope to apply it in community

“The relatively high accuracy of the deep learning models is exciting,” Lakhani said in the release. “The applicability for TB is important because it’s a condition for which we have treatment options. It’s a problem that we can solve.”

This is a reposting of my Scope blog story, courtesy of Stanford School of Medicine.