Background

Development and application of transcriptomics-based gene classifiers for ecotoxicological applications lag far behind those of biomedical sciences. Many such classifiers discovered thus far lack vigorous statistical and experimental validations. A combination of genetic algorithm/support vector machines and genetic algorithm/K nearest neighbors was used in this study to search for classifiers of endocrine-disrupting chemicals (EDCs) in zebrafish. Searches were conducted on both tissue-specific and tissue-combined datasets, either across the entire transcriptome or within individual transcription factor (TF) networks previously linked to EDC effects. Candidate classifiers were evaluated by gene set enrichment analysis (GSEA) on both the original training data and a dedicated validation dataset.

Results

Multi-tissue dataset yielded no classifiers. Among the 19 chemical-tissue conditions evaluated, the transcriptome-wide searches yielded classifiers for six of them, each having approximately 20 to 30 gene features unique to a condition. Searches within individual TF networks produced classifiers for 15 chemical-tissue conditions, each containing 100 or fewer top-ranked gene features pooled from those of multiple TF networks and also unique to each condition. For the training dataset, 10 out of 11 classifiers successfully identified the gene expression profiles (GEPs) of their targeted chemical-tissue conditions by GSEA. For the validation dataset, classifiers for prochloraz-ovary and flutamide-ovary also correctly identified the GEPs of corresponding conditions while no classifier could predict the GEP from prochloraz-brain.

Conclusions

The discrepancies in the performance of these classifiers were attributed in part to varying data complexity among the conditions, as measured to some degree by Fisher’s discriminant ratio statistic. This variation in data complexity could likely be compensated by adjusting sample size for individual chemical-tissue conditions, thus suggesting a need for a preliminary survey of transcriptomic responses before launching a full scale classifier discovery effort. Classifier discovery based on individual TF networks could yield more mechanistically-oriented biomarkers. GSEA proved to be a flexible and effective tool for application of gene classifiers but a similar and more refined algorithm, connectivity mapping, should also be explored. The distribution characteristics of classifiers across tissues, chemicals, and TF networks suggested a differential biological impact among the EDCs on zebrafish transcriptome involving some basic cellular functions.

Summary

Clustered Regularly Interspaced Short Palindromic Repeats (CRISPRs) and the associated proteins (Cas) comprise a system of adaptive immunity against viruses and plasmids in prokaryotes. Cas1 is a CRISPR-associated protein that is common to all CRISPR-containing prokaryotes but its function remains obscure. Here we show that the purified Cas1 protein of Escherichia coli (YgbT) exhibits nuclease activity against single-stranded and branched DNAs including Holliday junctions, replication forks, and 5′-flaps. The crystal structure of YgbT and site-directed mutagenesis have revealed the potential active site. Genome-wide screens show that YgbT physically and genetically interacts with key components of DNA repair systems, including recB, recC and ruvB. Consistent with these findings, the ygbT deletion strain showed increased sensitivity to DNA damage and impaired chromosomal segregation. Similar phenotypes were observed in strains with deletion of CRISPR clusters, suggesting that the function of YgbT in repair involves interaction with the CRISPRs. These results show that YgbT belongs to a novel, structurally distinct family of nucleases acting on branched DNAs and suggest that, in addition to antiviral immunity, at least some components of the CRISPR-Cas system have a function in DNA repair.

Cyclic diguanylate (c-di-GMP) is a ubiquitous second messenger regulating diverse cellular functions including motility, biofilm formation, cell cycle progression and virulence in bacteria. In the cell, degradation of c-di-GMP is catalyzed by highly specific EAL domain phosphodiesterases whose catalytic mechanism is still unclear. Here, we purified 13 EAL domain proteins from various organisms and demonstrated that their catalytic activity is associated with the presence of 10 conserved EAL domain residues. The crystal structure of the TDB1265 EAL domain was determined in a free state (1.8 Å) and in complex with c-di-GMP (2.35 Å) and unveiled the role of the conserved residues in substrate binding and catalysis. The structure revealed the presence of two metal ions directly coordinated by six conserved residues, two oxygens of the c-di-GMP phosphate, and potential catalytic water molecule. Our results support a two-metal-ion catalytic mechanism of c-di-GMP hydrolysis by EAL domain phosphodiesterases.