Genetic Rearrangements Can Modify Chromatin Features at Epialleles

Analogous to genetically distinct alleles, epialleles represent heritable states of different gene expression from sequence-identical genes. Alleles and epialleles both contribute to phenotypic heterogeneity. While alleles originate from mutation and recombination, the source of epialleles is less well understood. We analyze active and inactive epialleles that were found at a transgenic insert with a selectable marker gene in Arabidopsis. Both converse expression states are stably transmitted to progeny. The silent epiallele was previously shown to change its state upon loss-of-function of trans-acting regulators and drug treatments. We analyzed the composition of the epialleles, their chromatin features, their nuclear localization, transcripts, and homologous small RNA. After mutagenesis by T-DNA transformation of plants carrying the silent epiallele, we found new active alleles. These switches were associated with different, larger or smaller, and non-overlapping deletions or rearrangements in the 3′ regions of the epiallele. These cis-mutations caused different degrees of gene expression stability depending on the nature of the sequence alteration, the consequences for transcription and transcripts, and the resulting chromatin organization upstream. This illustrates a tight dependence of epigenetic regulation on local structures and indicates that sequence alterations can cause epigenetic changes at some distance in regions not directly affected by the mutation. Similar effects may also be involved in gene expression and chromatin changes in the vicinity of transposon insertions or excisions, recombination events, or DNA repair processes and could contribute to the origin of new epialleles

Endogenous Targets of Transcriptional Gene Silencing in Arabidopsis

Transcriptional gene silencing (TGS) frequently inactivates foreign genes integrated into plant genomes but very likely also suppresses an unknown subset of chromosomal information. Accordingly, RNA analysis of mutants impaired in silencing should uncover endogenous targets of this epigenetic regulation. We compared transcripts from wild-type Arabidopsis carrying a silent transgene with RNA from an isogenic transgene-expressing TGS mutant. Two cDNA clones were identified representing endogenous RNA expressed only in the mutant. The synthesis of these RNAs was found to be released in several mutants affected in TGS, implying that TGS in general and not a particular mutation controls the transcriptional activity of their templates. Detailed analysis revealed that the two clones are part of longer transcripts termed TSI (for transcriptionally silent information). Two major classes of related TSI transcripts were found in a mutant cDNA library. They are synthesized from repeats present in heterochromatic pericentromeric regions of Arabidopsis chromosomes. These repeats share sequence homology with the 3′ terminal part of the putative retrotransposon Athila. However, the transcriptional activation does not include the transposon itself and does not promote its movement. There is no evidence for a general release of silencing from retroelements. Thus, foreign genes in plants encounter the epigenetic control normally directed, at least in part, toward a subset of pericentromeric repeats

EEG synchronization measures are early outcome predictors in comatose patients after cardiac arrest.

OBJECTIVE Outcome prognostication in comatose patients after cardiac arrest (CA) remains a major challenge. Here we investigated the prognostic value of combinations of linear and non-linear bivariate EEG synchronization measures. METHODS 94 comatose patients with EEG within 24h after CA were included. Clinical outcome was assessed at 3months using the Cerebral Performance Categories (CPC). EEG synchronization between the left and right parasagittal, and between the frontal and parietal brain regions was assessed with 4 different quantitative measures (delta power asymmetry, cross-correlation, mutual information, and transfer entropy). 2/3 of patients were used to assess the predictive power of all possible combinations of these eight features (4 measures×2 directions) using cross-validation. The predictive power of the best combination was tested on the remaining 1/3 of patients. RESULTS The best combination for prognostication consisted of 4 of the 8 features, and contained linear and non-linear measures. Predictive power for poor outcome (CPC 3-5), measured with the area under the ROC curve, was 0.84 during cross-validation, and 0.81 on the test set. At specificity of 1.0 the sensitivity was 0.54, and the accuracy 0.81. CONCLUSION Combinations of EEG synchronization measures can contribute to early prognostication after CA. In particular, combining linear and non-linear measures is important for good predictive power. SIGNIFICANCE Quantitative methods might increase the prognostic yield of currently used multi-modal approaches