Scientist points to discarded data that sets research on track — ScienceDaily

Regular visits to the dentist can also help keep joint pain at bay.

When Rice University computational biologist Vicky Yao found traces of bacteria associated with periodontal disease in samples collected from rheumatoid arthritis patients, she wasn’t sure what to make of it.

Her finding helped inspire a series of experiments that confirmed a link between arthritis flares and periodontitis. The study is published in Translational Medicine Science.

Tracing this link between the two conditions could help develop therapies for rheumatoid arthritis, an autoimmune inflammatory disease that attacks the lining of joints and can cause heart, lung and eye problems . The approach that emerged from the study could be fruitful in other disease contexts, such as cancer.

“The data collected in experiments from living organisms or cells or tissues grown in Petri dishes are very important to confirm hypotheses, but, at the same time, these data may contain more information than we can derive immediately,” said Yao.

Yao’s hunger was confirmed when she took a deeper look at data collected by Dana Orange, associate professor of clinical investigation and rheumatologist, and Bob Darnell, professor and attending physician at Rockefeller University and the Howard Hughes Medical Institute from rheumatoid arthritis patients.

Yao was collaborating with Orange and others on a different project that tracked changes in gene expression during flares of rheumatoid arthritis.

“Orange, working with Darnell, collected data from arthritis patients regularly and, at the same time, monitored when the flares occurred,” said Yao. “The idea was that perhaps, looking at this data retrospectively, some pattern would emerge giving clues as to what might be causing the arthritis to flare up.

“While working on that project, I went to this talk that I thought was really cool because it showed that you can find traces of microbes in the data that is ignored or thrown away. sample but you get a glimpse of the microbes floating around. I was very interested in this.”

When she looked into it, Yao found that the germs in the samples had changed consistently throughout the patients before flare-ups mainly associated with gum disease.

“I was curious about this tool that allowed you to detect microbes in human samples. It was like, for free, you’re getting an extra perspective on the data,” Yao said. “At the time, I hadn’t done much work on microbial data at all. Since then, Dana took advantage of all this expertise and teamed up with people who studied these bacteria.

“One of the things that came up when we were discussing this was how cool it would be if you could prescribe some kind of mouthwash to help prevent rheumatoid arthritis flare-ups.”

Yao’s focus since joining Rice in 2019 has shifted to cancer research. The discovery of valuable information in data that would normally be ignored or discarded prompted Yao to take a similar approach when looking at data from cancer patients.

“I was very interested in what else we can find mining for microbial signatures in human samples,” said Yao. “Now, we’re doing something similar in looking at cancer.

“The hope here is that if we find some interesting microbial or viral signatures associated with cancer, we can identify productive experimental directions to follow. For example, if a tumor creates this hotbed of specific microbes that we identify, then we can we may be able to use that information as a means of diagnosing cancer earlier or in a less invasive or less expensive way. a link between a particular virus or bacteria and a type of cancer, so, of course, ‘could be useful for therapists.’

One of the best known examples of a pathogen associated with cancer is the human papillomavirus (HPV). Yao used this well-documented link to verify her approach.

“When we did the same exercise looking at cervical cancer tumor samples, we consistently detected the virus,” she said. “It’s a nice proof-of-principle result showing that the presence of specific pathogens can be important for certain types of cancer.

“I am very interested in using computational approaches to bridge the gap between available experimental data and ways to interpret it. Computational analysis is a way to help interpret data and prioritize hypotheses for clinicians or for experimental scientists to test.”

National Institutes of Health (R01 AR063676, R01 AR078268, U19 AI110491, U01 AI101981, T32 HG003284, R01 GM071966, T32 AI007290-35, P309 AR0706 National Science Foundation, Roberts University, Award, 1984, Science and Science Foundation, 2014 Roberts, University of Limerick, University of Limerick, University of Limerick, Galway) ), Bernard and Irene Schwartz Foundation, Iris and Jungming Le Foundation, Rockefeller Clinical and Translational Science Award Program Pilot Award, Rheumatology Research Foundation and National Cancer Institute (F30 CA243480) with the research.

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