Invariable Ribosome Stoichiometry During Murine Erythroid Differentiation: Implications for Understanding Ribosomopathies.

Heterogeneity of the main ribosomal composition represents an emerging, yet debatable, mechanism of gene expression regulation with a purported role in ribosomopathies, a group of disorders caused by mutations in ribosomal protein genes (RPs). Ribosomopathies, mysteriously relate with tissue-specific symptoms (mainly anemia and cancer predisposition), despite the ubiquitous expression and necessity of the associated RPs. An outstanding question that may shed light into disease pathogenicity and provide potential pharmacological interventions, is whether and how the ribosomal composition is modified during, the highly affected by RP mutations, process of erythroid differentiation. To address this issue, we analyzed ribosome stoichiometry using an established model of erythroid differentiation, through sucrose gradient ultracentrifugation and quantitative proteomics. We found that differentiation associates with an extensive reprogramming of the overall ribosomal levels, characterized by an increase in monosomes and a decrease in polysomes. However, by calculating a stoichiometry score for each independent ribosomal protein, we found that the main ribosomal architecture remained invariable between immature and differentiated cells. In total, none of the 78 Ribosomal Proteins (RPs- 74 core RPs, Rack1, Fau and 2 paralogs) detected was statistically different between the samples. This data was further verified through antibody-mediated quantification of 6 representative RPs. Moreover, bioinformatic analysis of whole cell proteomic data derived out of 4 additional models of erythropoiesis revealed that RPs were co-regulated across these cell types, too. In conclusion, ribosomes maintain an invariant protein stoichiometry during differentiation, thus excluding ribosome heterogeneity from a potential mechanism of toxicity in ribosomopathies and other erythroid disorders.

Dissecting miRNA-Gene Networks to Map Clinical Utility Roads of Pharmacogenomics-Guided Therapeutic Decisions in Cardiovascular Precision Medicine.

MicroRNAs (miRNAs) create systems networks and gene-expression circuits through molecular signaling and cell interactions that contribute to health imbalance and the emergence of cardiovascular disorders (CVDs). Because the clinical phenotypes of CVD patients present a diversity in their pathophysiology and heterogeneity at the molecular level, it is essential to establish genomic signatures to delineate multifactorial correlations, and to unveil the variability seen in therapeutic intervention outcomes. The clinically validated miRNA biomarkers, along with the relevant SNPs identified, have to be suitably implemented in the clinical setting in order to enhance patient stratification capacity, to contribute to a better understanding of the underlying pathophysiological mechanisms, to guide the selection of innovative therapeutic schemes, and to identify innovative drugs and delivery systems. In this article, the miRNA-gene networks and the genomic signatures resulting from the SNPs will be analyzed as a method of highlighting specific gene-signaling circuits as sources of molecular knowledge which is relevant to CVDs. In concordance with this concept, and as a case study, the design of the clinical trial GESS (NCT03150680) is referenced. The latter is presented in a manner to provide a direction for the improvement of the implementation of pharmacogenomics and precision cardiovascular medicine trials.

FOXO1 controls protein synthesis and transcript abundance of mutant polyglutamine proteins, preventing protein aggregation.

FOXO1, a transcription factor downstream of the insulin/insulin like growth factor axis, has been linked to protein degradation. Elevated expression of FOXO orthologs can also prevent the aggregation of cytosine adenine guanine (CAG)-repeat disease causing polyglutamine (polyQ) proteins but whether FOXO1 targets mutant proteins for degradation is unclear. Here, we show that increased expression of FOXO1 prevents toxic polyQ aggregation in human cells while reducing FOXO1 levels has the opposite effect and accelerates it. Although FOXO1 indeed stimulates autophagy, its effect on polyQ aggregation is independent of autophagy, ubiquitin-proteasome system (UPS) mediated protein degradation and is not due to a change in mutant polyQ protein turnover. Instead, FOXO1 specifically downregulates protein synthesis rates from expanded pathogenic CAG repeat transcripts. FOXO1 orchestrates a change in the composition of proteins that occupy mutant expanded CAG transcripts, including the recruitment of IGF2BP3. This mRNA binding protein enables a FOXO1 driven decrease in pathogenic expanded CAG transcript- and protein levels, thereby reducing the initiation of amyloidogenesis. Our data thus demonstrate that FOXO1 not only preserves protein homeostasis at multiple levels, but also reduces the accumulation of aberrant RNA species that may co-contribute to the toxicity in CAG-repeat diseases.

miR-16-5p Promotes Erythroid Maturation of Erythroleukemia Cells by Regulating Ribosome Biogenesis.

miRNAs constitute a class of non-coding RNA that act as powerful epigenetic regulators in animal and plant cells. In order to identify putative tumor-suppressor miRNAs we profiled the expression of various miRNAs during differentiation of erythroleukemia cells. RNA was purified before and after differentiation induction and subjected to quantitative RT-PCR. The majority of the miRNAs tested were found upregulated in differentiated cells with miR-16-5p showing the most significant increase. Functional studies using gain- and loss-of-function constructs proposed that miR-16-5p has a role in promoting the erythroid differentiation program of murine erythroleukemia (MEL) cells. In order to identify the underlying mechanism of action, we utilized bioinformatic in-silico platforms that incorporate predictions for the genes targeted by miR-16-5p. Interestingly, ribosome constituents, as well as ribosome biogenesis factors, were overrepresented among the miR-16-5p predicted gene targets. Accordingly, biochemical experiments showed that, indeed, miR-16-5p could modulate the levels of independent ribosomal proteins, and the overall ribosomal levels in cultured cells. In conclusion, miR-16-5p is identified as a differentiation-promoting agent in erythroleukemia cells, demonstrating antiproliferative activity, likely as a result of its ability to target the ribosomal machinery and restore any imbalanced activity imposed by the malignancy and the blockade of differentiation.

The histone methyltransferase inhibitor A-366 enhances hemoglobin expression in erythroleukemia cells upon co-exposure with chemical inducers in culture.

BACKGROUND: Erythroleukemia is caused by the uncontrolled multiplication of immature erythroid progenitor cells which fail to differentiate into erythrocytes. By directly targeting this class of malignant cells, the induction of terminal erythroid differentiation represents a vital therapeutic strategy for this disease. Erythroid differentiation involves the execution of a well-orchestrated gene expression program in which epigenetic enzymes play critical roles. In order to identify novel epigenetic mediators of differentiation, this study explores the effects of multiple, highly specific, epigenetic enzyme inhibitors, in murine and human erythroleukemia cell lines. RESULTS: We used a group of compounds designed to uniquely target the following epigenetic enzymes: G9a/GLP, EZH1/2, SMYD2, PRMT3, WDR5, SETD7, SUV420H1 and DOT1L. The majority of the probes had a negative impact on both cell proliferation and differentiation. On the contrary, one of the compounds, A-366, demonstrated the opposite effect by promoting erythroid differentiation of both cell models. A-366 is a selective inhibitor of the G9a methyltransferase and the chromatin reader Spindlin1. Investigation of the molecular mechanism of action revealed that A-366 forced cells to exit from the cell cycle, a fact that favored erythroid differentiation. Further analysis led to the identification of a group of genes that mediate the A-366 effects and include CDK2, CDK4 and CDK6. CONCLUSIONS: A-366, a selective inhibitor of G9a and Spindlin1, demonstrates a compelling role in the erythroid maturation process by promoting differentiation, a fact that is highly beneficial for patients suffering from erythroleukemia. In conclusion, this data calls for further investigation towards the delivery of epigenetic drugs and especially A-366 in hematopoietic disorders.

A Risk-Stratification Machine Learning Framework for the Prediction of Coronary Artery Disease Severity: Insights From the GESS Trial.

Our study aims to develop a data-driven framework utilizing heterogenous electronic medical and clinical records and advanced Machine Learning (ML) approaches for: (i) the identification of critical risk factors affecting the complexity of Coronary Artery Disease (CAD), as assessed via the SYNTAX score; and (ii) the development of ML prediction models for accurate estimation of the expected SYNTAX score. We propose a two-part modeling technique separating the process into two distinct phases: (a) a binary classification task for predicting, whether a patient is more likely to present with a non-zero SYNTAX score; and (b) a regression task to predict the expected SYNTAX score accountable to individual patients with a non-zero SYNTAX score. The framework is based on data collected from the GESS trial (NCT03150680) comprising electronic medical and clinical records for 303 adult patients with suspected CAD, having undergone invasive coronary angiography in AHEPA University Hospital of Thessaloniki, Greece. The deployment of the proposed approach demonstrated that atherogenic index of plasma levels, diabetes mellitus and hypertension can be considered as important risk factors for discriminating patients into zero- and non-zero SYNTAX score groups, whereas diastolic and systolic arterial blood pressure, peripheral vascular disease and body mass index can be considered as significant risk factors for providing an accurate estimation of the expected SYNTAX score, given that a patient belongs to the non-zero SYNTAX score group. The experimental findings utilizing the identified set of important risk factors indicate a sufficient prediction performance for the Support Vector Machine model (classification task) with an F-measure score of ~0.71 and the Support Vector Regression model (regression task) with a median absolute error value of ~6.5. The proposed data-driven framework described herein present evidence of the prediction capacity and the potential clinical usefulness of the developed risk-stratification models. However, further experimentation in a larger clinical setting is needed to ensure the practical utility of the presented models in a way to contribute to a more personalized management and counseling of CAD patients.

Pharmacogenomic Testing to Guide Personalized Cancer Medicine Decisions in Private Oncology Practice: A Case Study.

Innovative tumor profiling methodologies are utilized to elucidate the pharmacogenomic landscape of tumor cells in order to support the molecularly guided delivery of therapeutics. Indeed, improved clinical outcomes are achieved in oncology practice by providing the physicians with expert-guided, standardized, and easily interpretable knowledge, translated from molecular profiling analysis to support clinical decision-making. However, there is still limited utilization of the technology especially in small private oncology practices. In this work, we analyzed how molecularly guided interventions in 17 consented cancer patients led to an overall improvement of disease response rates in a private oncology center. The precision medicine strategy was based on the OncoDEEP™ profiling solutions and focused on finding clinically actionable relationships between tumor biomarkers and drug responses. The obtained data support the notion that (a) following the pharmacogenomic-derived recommendations favorably impacted cancer therapy progression, and (b) the earlier profiling followed by the delivery of molecularly targeted therapy led to more durable and improved pharmacological response rates. Moreover, we report the example of a patient with metastatic gastric adenocarcinoma who, based on the molecular profiling data, received an off-label therapy that resulted in a complete response and a current cancer-free maintenance status. Overall, our data provide a paradigm on how molecular tumor profiling can improve decision-making in the routine private oncology practice.