Selective Inhibition of the Master Regulator Transcription Factor Egr‐1 With Catalytic Oligonucleotides Reduces Myocardial Injury and Improves Left Ventricular Systolic Function in a Preclinical Model of Myocardial Infarction

Rayner, Benjamin S.; Figtree, Gemma A.; Sabaretnam, Tharani; Shang, Peng; Mazhar, J.; Weaver, James; Lay, William; Witting, Paul K.; Hunyor, Stephen N.; Grieve, Stuart M.; Khachigian, Levon M.; Bhindi, Ravinay

doi:10.1161/jaha.113.000023

Cited by 30 publications

(19 citation statements)

References 20 publications

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“…In addition, it would be useful if future studies expanded the outcomes beyond the four considered here to gain more insight into the range over which prediction occurs. Such an investigation could be carried out informally using the simple correlational methods employed here or more formally using machine learning methods developed to discover genetic master regulators (Chan & Kyba, 2013; Rabinowitz & Silhavy, 2013; Rayner et al ., 2013; Ryu et al ., 2007). …”

Section: Discussionmentioning

confidence: 99%

The effects of co-morbidity in defining major depression subtypes associated with long-term course and severity

et al. 2014

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Background Although variation in long-term course of major depressive disorder (MDD) is not strongly predicted by existing symptom subtype distinctions, recent research suggests that prediction can be improved by using machine learning methods. However, it is not known whether these distinctions can be refined by added information about comorbid conditions. The current report presents results on this question. Methods Data come from 8,261 respondents with lifetime DSM-IV MDD in the WHO World Mental Health (WMH) Surveys. Outcomes include four retrospectively-reported measures of persistence-severity of course (years in episode; years in chronic episodes, hospitalization for MDD; disability due to MDD). Machine learning methods (regression tree analysis; lasso, ridge, and elastic net penalized regression) followed by k-means cluster analysis were used to augment previously-detected subtypes with information about prior comorbidity to predict these outcomes. Results Predicted values were strongly correlated across outcomes. Cluster analysis of predicted values found 3 clusters with consistently high, intermediate, or low values. The high-risk cluster (32.4% of cases) accounted for 56.6–72.9% of high persistence, high chronicity, hospitalization, and disability. This high-risk cluster had both higher sensitivity and likelihood-ratio positive (relative proportions of cases in the high-risk cluster versus other clusters having the adverse outcomes) than in a parallel analysis that excluded measures of comorbidity as predictors. Conclusions Although results using the retrospective data reported here suggest that useful MDD subtyping distinctions can be made with machine learning and clustering across multiple indicators of illness persistence-severity, replication is need with prospective data to confirm this preliminary conclusion.

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Section: Discussionmentioning

confidence: 99%

The effects of co-morbidity in defining major depression subtypes associated with long-term course and severity

et al. 2014

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“…[59][60][61] Or it might be possible to address this issue by adapting the data mining methods developed to discover what have been called master regulators 62 in molecular genetic studies of physical disorders. [63][64][65] Regardless of method, though, the discovery of common predictors of multiple indicators of persistence, chronicity, and severity call out for a more diverse and integrated analysis of clusters and within-cluster subtypes among the predictors of such outcomes.…”

Section: Depression and Anxietymentioning

confidence: 99%

Major Depressive Disorder Subtypes to Predict Long-Term Course

et al. 2014

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Background Variation in course of major depressive disorder (MDD) is not strongly predicted by existing subtype distinctions. A new subtyping approach is considered here. Methods Two data mining techniques, ensemble recursive partitioning and Lasso generalized linear models (GLMs) followed by k-means cluster analysis, are used to search for subtypes based on index episode symptoms predicting subsequent MDD course in the World Mental Health (WMH) Surveys. The WMH surveys are community surveys in 16 countries. Lifetime DSM-IV MDD was reported by 8,261 respondents. Retrospectively reported outcomes included measures of persistence (number of years with an episode; number of with an episode lasting most of the year) and severity (hospitalization for MDD; disability due to MDD). Results Recursive partitioning found significant clusters defined by the conjunctions of early onset, suicidality, and anxiety (irritability, panic, nervousness-worry-anxiety) during the index episode. GLMs found additional associations involving a number of individual symptoms. Predicted values of the four outcomes were strongly correlated. Cluster analysis of these predicted values found three clusters having consistently high, intermediate, or low predicted scores across all outcomes. The high-risk cluster (30.0% of respondents) accounted for 52.9-69.7% of high persistence and severity and was most strongly predicted by index episode severe dysphoria, suicidality, anxiety, and early onset. A total symptom count, in comparison, was not a significant predictor. Conclusions Despite being based on retrospective reports, results suggest that useful MDD subtyping distinctions can be made using data mining methods. Further studies are needed to test and expand these results with prospective data.

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“…Expression of Egr-1 has been previously linked to several aspects of cardiovascular pathology including intimal thickening following acute vascular injury [118], cardiac hypertrophy [119], atherosclerosis [120] and angiogenesis [121]. Doxorubicin induced cardiomyopathy is also mediated by Egr-1 [122] and targeting Egr-1 reduces the pathological effects of acute myocardial infarction in rats [123].…”

Section: Direct Acetylation Of Transcription Factors In Heartmentioning

confidence: 99%

Mechanisms of transcription factor acetylation and consequences in hearts

Thiagarajan

Vedantham

Ananthakrishnan

et al. 2016

Biochimica et Biophysica Acta (BBA) - Molecular Basis of Diseas

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Acetylation of proteins as a post-translational modification is gaining rapid acceptance as a cellular control mechanism on par with other protein modification mechanisms such as phosphorylation and ubiquitination. Through genetic manipulations and evolving proteomic technologies, identification and consequences of transcription factor acetylation is beginning to emerge. In this review, we summarize the field and discuss newly unfolding mechanisms and consequences of transcription factor acetylation in normal and stressed hearts.

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Selective Inhibition of the Master Regulator Transcription Factor Egr‐1 With Catalytic Oligonucleotides Reduces Myocardial Injury and Improves Left Ventricular Systolic Function in a Preclinical Model of Myocardial Infarction

Cited by 30 publications

References 20 publications

The effects of co-morbidity in defining major depression subtypes associated with long-term course and severity

The effects of co-morbidity in defining major depression subtypes associated with long-term course and severity

Major Depressive Disorder Subtypes to Predict Long-Term Course

Mechanisms of transcription factor acetylation and consequences in hearts

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