Chemiluminescent Biosensors for Detection of Second Messenger Cyclic di-GMP.

Bacteria colonize highly diverse and complex environments, from gastrointestinal tracts to soil and plant surfaces. This colonization process is controlled in part by the intracellular signal cyclic di-GMP, which regulates bacterial motility and biofilm formation. To interrogate cyclic di-GMP signaling networks, a variety of fluorescent biosensors for live cell imaging of cyclic di-GMP have been developed. However, the need for external illumination precludes the use of these tools for imaging bacteria in their natural environments, including in deep tissues of whole organisms and in samples that are highly autofluorescent or photosensitive. The need for genetic encoding also complicates the analysis of clinical isolates and environmental samples. Toward expanding the study of bacterial signaling to these systems, we have developed the first chemiluminescent biosensors for cyclic di-GMP. The biosensor design combines the complementation of split luciferase (CSL) and bioluminescence resonance energy transfer (BRET) approaches. Furthermore, we developed a lysate-based assay for biosensor activity that enabled reliable high-throughput screening of a phylogenetic library of 92 biosensor variants. The screen identified biosensors with very large signal changes (∼40- and 90-fold) as well as biosensors with high affinities for cyclic di-GMP ( KD < 50 nM). These chemiluminescent biosensors then were applied to measure cyclic di-GMP levels in E. coli. The cellular experiments revealed an unexpected challenge for chemiluminescent imaging in Gram negative bacteria but showed promising application in lysates. Taken together, this work establishes the first chemiluminescent biosensors for studying cyclic di-GMP signaling and provides a foundation for using these biosensors in more complex systems.

Estimating parameters of a forest ecosystem C model with measurements of stocks and fluxes as joint constraints.

We conducted an inverse modeling analysis, using a variety of data streams (tower-based eddy covariance measurements of net ecosystem exchange, NEE, of CO2, chamber-based measurements of soil respiration, and ancillary ecological measurements of leaf area index, litterfall, and woody biomass increment) to estimate parameters and initial carbon (C) stocks of a simple forest C-cycle model, DALEC, using Monte Carlo procedures. Our study site is the spruce-dominated Howland Forest AmeriFlux site, in central Maine, USA. Our analysis focuses on: (1) full characterization of data uncertainties, and treatment of these uncertainties in the parameter estimation; (2) evaluation of how combinations of different data streams influence posterior parameter distributions and model uncertainties; and (3) comparison of model performance (in terms of both predicted fluxes and pool dynamics) during a 4-year calibration period (1997-2000) and a 4-year validation period ("forward run", 2001-2004). We find that woody biomass increment, and, to a lesser degree, soil respiration, measurements contribute to marked reductions in uncertainties in parameter estimates and model predictions as these provide orthogonal constraints to the tower NEE measurements. However, none of the data are effective at constraining fine root or soil C pool dynamics, suggesting that these should be targets for future measurement efforts. A key finding is that adding additional constraints not only reduces uncertainties (i.e., narrower confidence intervals) on model predictions, but at the same time also results in improved model predictions by greatly reducing bias associated with predictions during the forward run.