• NIH, 2006-2010, Bacterial Response to Singlet Oxygen
• Department of Energy, 2007-2010, Networks Impacting Solar Powered Hydrogen Production (co-PI with Daniel Noguera, Department of Civil & Environmental Engineering)
• Department of Energy, 2007-2010 Great Lakes Bioenergy Research Center

• 5,747,328 - Microbial System for Formaldehyde Sensing and Remediation, granted May 1998.
• 5,834,300 - Microbial system for formaldehyde sensing and remediation, granted Nov 1998.
• 5,837,481 - Microbial system for formaldehyde sensing and remediation, granted Nov 1998.

• "H-NOX-mediated nitric oxide sensing modulates symbiotic colonization by Vibrio fischeri"

Wang Y, Dufour YS, Carlson HK, et al., Proceedings of the National Academy of Sciences of the United States of America 107 (18): 8375-8380 May 2010.  Our data suggest that V. fischeri normally senses a host-generated NO signal through H-NOX(Vf) and modulates the expression of its iron uptake capacity during the early stages of the light-organ symbiosis.  PubMed/NLM.

• "Bacterial responses to photo-oxidative stress"

Ziegelhoffer EC, Donohue TJ. Nature Reviews Microbiology 7(12): 856-863 December 2009. Singlet oxygen is one of several reactive oxygen species that can destroy biomolecules, microorganisms and other cells. Traditionally, the response to singlet oxygen has been termed photo-oxidative stress, as light-dependent processes in photosynthetic cells are major biological sources of singlet oxygen.

• "Targeted sigma factor turnover inserts negative control into a positive feedback loop"

Donohue TJ. MOlecular Microbiology 73(5):747-750 September 2009. P>Since their classification as members of the sigma(70) superfamily, Group IV alternative sigma factors have been found to control gene expression in response to diverse environmental or stress signals.

• "A computational strategy to analyze label-free temporal bottom-up proteomics data"

Du X, Callister SJ, Manes NP, et al., Journal of Proteome Research 7 (7): 2595-2604 Jul 2008. This paper describes a strategy that addresses issues of protein abundance dynamics and demonstrates its values for analyzing temporal bottom-up proteomics data using data from a Rhodobacter sphaeroides 2.4.1 time-course study.