Ulvan is the main polysaccharide component of the Ulvales (green seaweed) cell wall. It is composed of disaccharide building blocks comprising 3-sulfated rhamnose linked to D-glucuronic acid (GlcUA), L-iduronic acid (IdoUA), or D-xylose (Xyl). The degradation of ulvan requires ulvan lyase, which catalyzes the endolytic cleavage of the glycoside bond between 3-sulfated rhamnose and uronic acid according to a ␤-elimination mechanism. The first characterized ulvan lyase was identified in Nonlabens ulvanivorans, an ulvanolytic bacterial isolate. In the current study, we have identified and biochemically characterized novel ulvan lyases from three Alteromonadales isolated bacteria. Two homologous ulvan lyases (long and short) were found in each of the bacterial genomes. The protein sequences have no homology to the previously reported ulvan lyases and therefore are the first representatives of a new family of polysaccharide lyases. The enzymes were heterologously expressed in Escherichia coli to determine their mode of action. The heterologous expressed enzymes were secreted into the milieu subsequent to their signal sequence cleavage. An endolytic mode of action was observed and studied using gel permeation chromatography and 1 H NMR. In contrast to N. ulvanivorans ulvan lyase, cleavage occurred specifically at the GlcUA residues. In light of the genomic context and modular structure of the ulvan lyase families identified to date, we propose that two ulvan degradation pathways evolved independently.

Pseudomonas aeruginosa is a Gram-negative, opportunistic pathogen that causes chronic and acute infections in immunocompromised patients. Most P. aeruginosa strains encode an active type III secretion system (T3SS), utilized by the bacteria to deliver effector proteins from the bacterial cell directly into the cytoplasm of the host cell. Four T3SS effectors have been discovered and extensively studied in P. aeruginosa: ExoT, ExoS, ExoU, and ExoY. This is especially intriguing in light of P. aeruginosa’s ability to infect a wide range of hosts. We therefore hypothesized that additional T3SS effectors that have not yet been discovered are encoded in the genome of P. aeruginosa. Here, we applied a machine learning classification algorithm to identify novel P. aeruginosa effectors. In this approach, various types of data are integrated to differentiate effectors from the rest of the open reading frames of the bacterial genome. Due to the lack of a sufficient learning set of positive effectors, our machine learning algorithm integrated genomic information from another Pseudomonas species and utilized dozens of features accounting for various aspects of the effector coding genes and their products. Twelve top-ranking predictions were experimentally tested for T3SS-specific translocation, leading to the discovery of two novel T3SS effectors. We demonstrate that these effectors are not part of the injection structural complex and report initial efforts toward their characterization.