- Deep Learning Model Identifies Drug to Combat Antibiotic Resistance
Symptoms of pneumococcal infections can include fever, cough, shortness of breath, chest pain, stiff neck, confusion, increased sensitivity to light, joint pain, chills, ear pain, sleeplessness, and irritability, researchers noted. In 2000, the emergence of the first pneumococcal vaccine lowered the number of deaths attributable to these infections.
However, increasing antibiotic resistance has made some of these infections more difficult to treat. Antibiotic resistance occurs when bacteria, viruses, fungi, and parasites change over time and no longer respond to medicines, making infections harder to treat and increasing the risk of disease spread, illness, and death.
Researchers from the University of Maryland School of Medicine collaborated with teams from the University of Alabama at Birmingham and Yale University School of Medicine to analyze gene expression in various infection sites, such as the nose and throat passages, the heart, bloodstream, lungs, and kidneys.
Researchers then developed an atlas from this gene expression data and found that the bacteria behaves differently depending on which site it infects within the mouse model, and mouse organs respond differently as well.
Additionally, the team found that certain S. pneumoniae genes were always highly expressed at all anatomical sites, which makes them ideal targets for new vaccines or therapies. In an animal challenge experiment, researchers found that an anti-inflammatory treatment called interferon beta worked to prevent the bacteria from invading vital organs. This discovery could open doors for novel treatment methods.
"This promoted host survival and provided us with important insights into potentially new avenues for treatment," said study co-author Adonis D'Mello, a graduate student in molecular medicine at the Institute for Genome Sciences. "We were able to build upon analytical pipelines to provide a more comprehensive way of studying diverse systemic pathogens."
The findings demonstrate the potential for the database to help researchers find new treatments for bacterial infections.
“We believe that the atlas of transcriptional responses during host–pathogen interactions presented here will constitute an essential resource for the pneumococcal and microbial pathogenesis research communities, and serve as a foundation for identification and validation of key host and pneumococcal therapeutic targets in future studies,” researchers stated.
The team noted that not all pneumococcal and mouse genes examined in the study may be directly relevant to humans, and the group may have missed other important genes in their analysis. However, despite these limitations, researchers expect that their results could help broaden treatment options and reduce antibiotic resistance.
"This is a very exciting basic research finding that could have widespread implications in our understanding of a widespread and potentially dangerous infectious disease," said E. Albert Reece, MD, PhD, MBA, Executive Vice President for Medical Affairs, UM Baltimore, and the John Z. and Akiko K. Bowers Distinguished Professor and Dean, University of Maryland School of Medicine.
"It also has broader applicability for the field of transcriptome research to identify potential new treatments for diseases."
By Jessica Kent