Three closely related nematophagous fungi in the genus Hyalorbilia were compared for their ability to parasitize females and eggs of Heterodera schachtii at different developmental stages. DoUCR50, StM, and ARF were originally isolated from Heterodera schachtii, Meloidogyne incognita, and Heterodera glycines, respectively. Phylogenetic analysis and pairwise sequence analysis showed that DoUCR50 and StM are more closely related to each other than they are to ARF. DoUCR50 parasitism suppressed 100% of the J2 hatch from 3-week-old H. schachtii females and 75% of the hatch from 4-week-old females. Eggs within 5-week-old females were resistant to parasitism, and hatch of J2 was unaffected by exposure to DoUCR50. StM and ARF did not reduce the hatch of J2 from H. schachtii females of any age. Eggs removed from females and spread onto water agar cultures of the fungi were mostly resistant to parasitism. DoUCR50 parasitized only 16% of such eggs from 3-week-old females. Extracellular hydrolytic enzyme production by the three fungal strains grown on PDA or parasitized H. schachtii females was evaluated using API ZYM (bioMérieux) test strips. All three fungi produced extracellular hydrolytic enzymes when grown on PDA or H. schachtii females. Trypsin-like protease activity was uniquely detected in DoUCR50 grown on PDA and H. schachtii females, with the highest activity associated with the fungus grown on parasitized females.

We study the problem of selecting control clones in DNA array hybridization experiments. The problem arises in the OFRG method for analyzing microbial communities. The OFRG method performs classification of rRNA gene clones using binary fingerprints created from a series of hybridization experiments, where each experiment consists of hybridizing a collection of arrayed clones with a single oligonucleotide probe. This experiment produces analog signals, one for each clone, which then need to be classified, that is, converted into binary values 1 and 0 that represent hybridization and non-hybridization events. Besides the sample clones, the array contains a number of control clones needed to calibrate the classification procedure of the hybridization signals. These control clones must be selected with care to optimize the classification process. We formulate this as a combinatorial optimization problem called Balanced Covering. We prove that the problem is NP-hard and we show some results on hardness of approximation. We propose an approximation algorithm based on randomized rounding and we show that, with high probability, it approximates well the optimum. The experimental results confirm that the algorithm finds high quality control clones. The algorithm has been implemented and is publicly available as part of the software package called CloneTools.