A new model for understanding immune responses to influenza
Multidrug-resistant pathogens can flourish in the environment and scientists increasingly are able to predict how infectious viral strains will evolve to gain immunity and how pathogen tolerance varies among humans.
But if the immune system does not work correctly the pathogens may be able to accumulate a gene mutation that leads to resistance and become resistant again.
That means the only way to limit the potential for the next flu pandemic to happen is to immunize against the challenge posed by emerging flu strains. Failure to do so would mean a fresh wave of infections.
Now a research team at the University at Buffalos Dave and Rand Miller School of Engineering and Applied Science (DMS) is taking the next step in understanding this complex issue by providing a new mathematical model of influenza immunology. With this model they can predict with certainty that the characteristics of emerging strains will evolve to fend off the deadliest strains of influenza.
This is a critical step in the evolution of influenza immunology and how to cope with resistant pathogens like influenza A said PhD candidate Shweta Choudhary co-principal investigator on the study.
Published in the PLOS ONE journal the teams work represents the first time they have been able to incorporate the behavior of the immune system in a mathematical model to predict the strategies for defeating pathogens that have become resistant approximately 10000 times faster than the human immune system.
Currently flu models are analyzed via the use of information from experimental data. However the desire to develop human-based models to provide guidance on what to do about emerging strains was realized when Choudhary realized she could analyze the larger data set she had available to develop a mathematical model of the influenza immunology based on the current influenza data.
Examining and synthesizing information from the large population of experimental patients that participated in the current study our collaborator Dr. Bin-Wen Lung decided to use statistical modeling to enhance our skills said Choudhary.
Using this approach the team was able to integrate information spanning across all available models of the influenza immune system to predict fundamental advances in dealing with the risk posed by emerging evolutionary novel coronaviruses.
For the first time we were able to predict the evolution of the most common way of battling a common type of influenza virus namely adaptive immunity (immune responses that are driven by self-tolerance innate immunity) Choudhary said. Without better understanding the nature of this adaptive immunity it was unclear how the adaptive strategies evolved to fight the huge number of different influenza viruses currently known.
The researchers found that when considering a particular model immune evolution could be categorized into two paths.
In the direction of immune evolution driven by adaptive immunity we learned that fighting many more influenza viruses appeared on the side of apomoean and thus we can be very certain about being able to predict which influenza viruses are going to become resistant and vice versa with a high degree of accuracy Choudhary said.
In the direction of immunity driven by adaptive resistance we learned that there are still quite a lot of unknowns surrounding the dynamics and mechanisms behind adaptive immunity. By undertaking a lot of work we were able to predict which of the sweeping T cell responses are likely to be successful and which ones are likely to be ineffective. said Choudhary. If we assess the effects of adaptive immunity and a broad T cell response are effective we may find that adaptive immune systems for many diseases such as autoimmune disease and cancer may be organized into a single type with greater power in improving response. In addition we also learn that adaptive immunity protects against a large number of CAV strains with the ability to rapidly evolve and redefine the nature of the disease.