Toxic interactions of metal ions (Cd2+, Pb2+, Zn2+ and Sb3- on in vitro biomass production of ectomycorrhizal fungi

A number of ectomycorrhizal (ECM) fungi, from sites uncontaminated by toxic metals, were investigated to determine their sensitivity to Cd^2-, Pb²⁺, Zn²⁺ and Sb^3-, measured as an inhibition of fungal biomass production. Isolates were grown in liquid media amended with the metals, individually (over a range of concentrations) and in combination (at single concentrations) to determine any significant interactions between the metals. Significant interspecific variation in sensitivity to Cd²⁺ and Zn²⁺ was recorded, while Pb²⁺ and Sb^3- individually had little effect. The presence of Pb²⁺ and Sb^3- in the media did however, ameliorate Cd²⁺ and Zn²⁺ toxicity in some circumstances. Interactions between Cd²⁺ and Zn²⁺ were investigated further over a range of concentrations. Zn²⁺ was found to significantly ameliorate the toxicity of Cd²⁺ to three of the four isolates tested. The influence of Zn²⁺ varied between ECM species and with the concentrations of metals tested.

Biotransformation of 2,4,6-trinitrotoluene (TNT) by ectomycorrhizal basidiomycetes

The ability of four ectomycorrhizal basidiomycetes to biotransform 2,4,6-trinitrotoluene (TNT) in axenic culture was tested. All species were capable of TNT biotransformation to a greater or lesser extent. When biotransformation was expressed on a biomass basis 4 out of the 5 isolates tested were equally efficient at transforming TNT. The factors regulating TNT biotransformation were investigated in detail for one fungus, Suillus variegatus. When the fungus was grown under nitrogen limiting conditions the rate of biotransformation decreased relative to nitrogen sufficient conditions, but no decrease was observed under short term carbon starvation. Extracellular enzymes of S. variegatus could transform TNT, but transformation was greater in intact cells. The mycelial cell wall fraction did not degrade TNT. The TNT concentration that caused 50% reduction in biomass (EC₅₀) for S. variegatus was within the range observed for other basidiomycete fungi being between 2-10 μg mL^-1. The potential use of ectomycorrhizal basidiomycetes as in-situ bioremediation agents for TNT contaminated soils is discussed.

Do ectomycorrhizal fungi exhibit adaptive tolerance to potentially toxic metals in the environment?

The effects of potentially toxic metals on ectomycorrhizal (ECM) fungi and their higher plant hosts are examined in this review. Investigations at a species and community level have revealed wide inter- and intraspecific variation in sensitivity to metals. Adaptive and constitutive mechanisms of ECM tolerance are proposed and discussed in relation to proven tolerance mechanisms in bacteria, yeasts and plants. Problems with methodology and research priorities are highlighted. These include the need for a detailed understanding of the genetic basis of tolerance in the ECM symbiosis, and for studies of ECM community dynamics in polluted sites.

Mineralization of 2,4-dichlorophenol by ectomycorrhizal fungi in axenic culture and in symbiosis with pine

Experiments were conducted to determine if two ectomycorrhizal fungi (Paxillus involutus and Suillus variegatus) could degrade 2,4-dichlorophenol both in axenic liquid culture and during symbiosis with a host tree species Pinus sylvestris. Both fungi readily degraded 2,4- dichlorophenol in batch culture with similar rates of mineralization on a biomass basis. Up to 17% of the 2,4-dichlorophenol was mineralized over a 17 day period. Growth of the fungi in symbiosis with P. sylvestris stimulated greater mineralization than when fungi were grown in absence of the host. S. variegatus was more efficient than P. involutus (in the presence of P. sylveslris) at mineralizing 2,4- dichlorophenol. Mineralization in vermiculite culture was greatly reduced compared to liquid culture. Only 3% of the 2,4-dichlorophenol was mineralized after 13 days in vermiculite culture for the most efficient degrading treatment.

Meta-analysis of prostate cancer gene expression data identifies a novel discriminatory signature enriched for glycosylating enzymes

BACKGROUND: Tumorigenesis is characterised by changes in transcriptional control. Extensive transcript expression data have been acquired over the last decade and used to classify prostate cancers. Prostate cancer is, however, a heterogeneous multifocal cancer and this poses challenges in identifying robust transcript biomarkers.

METHODS: In this study, we have undertaken a meta-analysis of publicly available transcriptomic data spanning datasets and technologies from the last decade and encompassing laser capture microdissected and macrodissected sample sets.

RESULTS: We identified a 33 gene signature that can discriminate between benign tissue controls and localised prostate cancers irrespective of detection platform or dissection status. These genes were significantly overexpressed in localised prostate cancer versus benign tissue in at least three datasets within the Oncomine Compendium of Expression Array Data. In addition, they were also overexpressed in a recent exon-array dataset as well a prostate cancer RNA-seq dataset generated as part of the The Cancer Genomics Atlas (TCGA) initiative. Biologically, glycosylation was the single enriched process associated with this 33 gene signature, encompassing four glycosylating enzymes. We went on to evaluate the performance of this signature against three individual markers of prostate cancer, v-ets avian erythroblastosis virus E26 oncogene homolog (ERG) expression, prostate specific antigen (PSA) expression and androgen receptor (AR) expression in an additional independent dataset. Our signature had greater discriminatory power than these markers both for localised cancer and metastatic disease relative to benign tissue, or in the case of metastasis, also localised prostate cancer.

CONCLUSION: In conclusion, robust transcript biomarkers are present within datasets assembled over many years and cohorts and our study provides both examples and a strategy for refining and comparing datasets to obtain additional markers as more data are generated.