Cadmium is a highly toxic heavy metal for biological systems. Cupriavidus metallidurans CH34 is a model strain for heavy metal resistance and bioremediation. The aim of this study was to determine the role of the c-di-GMP pathway in the C. metallidurans CH34 response to cadmium in both planktonic and biofilm cells. Increasing cadmium concentrations correlates with an inhibition of biofilm formation and EPS production in C. metallidurans cells. Planktonic and biofilm cells showed similar tolerance to cadmium. During exposure to cadmium an acute decrease of c-di-GMP levels in planktonic and biofilm cells was observed. Transcription analysis by RT-qPCR showed that cadmium induced in planktonic cells and strongly induced in biofilm cells the expression of the urf2 gene and the mercuric reductase encoding merA gene, which belong to the Tn501/Tn21 mer operon. After exposure to cadmium the cadA gene involved in cadmium resistance was equally upregulated in both lifestyles. Bioinformatic analysis and null mutant complementation assays indicated that the protein encoded by the urf2 gene is a functional phosphodiesterase involved in the c-di-GMP metabolism. We propose to rename the urf2 gene as mrp gene for metal regulated phosphodiesterase. An increase of the second messenger c-di-GMP content by the heterologous expression of the constitutively active diguanylate cyclase PleD* correlated with an increase in biofilm formation and cadmium susceptibility. These results indicate that the response to cadmium in C. metallidurans CH34 involves a decrease in c-di-GMP content that inhibits the biofilm lifestyle.

In this paper, by extending some results of informational genomics, we present a new randomness test based on the empirical entropy of strings and some properties of repeatability and unrepeatability of substrings of certain lengths. We give the theoretical motivations of our method and some experimental results of its application to a wide class of strings: decimal representations of real numbers, roulette outcomes, logistic maps, linear congruential generators, quantum measurements, natural language texts, and genomes. It will be evident that the evaluation of randomness resulting from our tests does not distinguish among the different sources of randomness (natural, or pseudo-casual).