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Gonzalez, A. J.

Publications and source records attributed to Gonzalez, A. J..

2 recordsLinked to original sources

Attenuation of influenza A virus disease severity by viral co-infection in a mouse model

Influenza viruses and rhinoviruses are responsible for a large number of acute respiratory viral infections in human populations and are detected as co-pathogens within hosts. Clinical and epidemiological studies suggest that co-infection by rhinovirus and influenza virus may reduce disease severity and that they may also interfere with each others spread within a host population. To determine how co-infection by these two unrelated respiratory viruses affects pathogenesis, we established a mouse model using a minor serogroup rhinovirus (RV1B) and mouse-adapted influenza A virus (PR8). Infection of mice with RV1B two days before PR8 reduced pathogenesis of mild to moderate, but not severe PR8 infections. Disease attenuation was associated with an early inflammatory response in the lungs and enhanced clearance of PR8. However, co-infection by RV1B did not reduce PR8 viral loads early in infection or inhibit replication of PR8 within respiratory epithelia or in vitro. Inflammation in co-infected mice remained focal, in comparison to diffuse inflammation and damage in the lungs of mice infected by PR8. These findings suggest that RV1B stimulates an early immune response that clears PR8 while limiting excessive pulmonary inflammation. The timing of RV1B co-infection was a critical determinant of protection, suggesting that sufficient time is needed to induce this response. Finally, disease attenuation was not unique to RV1B: co-infection by a murine coronavirus two days before PR8 also reduced disease severity. This model will be critical for understanding the mechanisms responsible for attenuation of influenza disease during co-infection by unrelated respiratory viruses.

microbiology

Novel antimicrobial peptide discovery using machine learning and biophysical selection of minimal bacteriocin domains

Bacteriocins are ribosomally produced antimicrobial peptides that represent an untapped source of promising antibiotic alternatives. However, inherent challenges in isolation and identification of natural bacteriocins in substantial yield have limited their potential use as viable antimicrobial compounds. In this study, we have developed an overall pipeline for bacteriocin-derived compound design and testing that combines sequence-free prediction of bacteriocins using a machine-learning algorithm and a simple biophysical trait filter to generate minimal 20 amino acid peptide candidates that can be readily synthesized and evaluated for activity. We generated 28,895 total 20-mer peptides and scored them for charge, -helicity, and hydrophobic moment, allowing us to identify putative peptide sequences with the highest potential for interaction and activity against bacterial membranes. Of those, we selected sixteen sequences for synthesis and further study, and evaluated their antimicrobial, cytotoxicity, and hemolytic activities. We show that bacteriocin-based peptides with the overall highest scores for our biophysical parameters exhibited significant antimicrobial activity against E. coli and P. aeruginosa. Our combined method incorporates machine learning and biophysical-based minimal region determination, to create an original approach to rapidly discover novel bacteriocin candidates amenable to rapid synthesis and evaluation for therapeutic use.

microbiology