DAXX promotes centromeric stability independently of ATRX by preventing the accumulation of R-loop-induced DNA double-stranded breaks.

Maintaining chromatin integrity at the repetitive non-coding DNA sequences underlying centromeres is crucial to prevent replicative stress, DNA breaks and genomic instability. The concerted action of transcriptional repressors, chromatin remodelling complexes and epigenetic factors controls transcription and chromatin structure in these regions. The histone chaperone complex ATRX/DAXX is involved in the establishment and maintenance of centromeric chromatin through the deposition of the histone variant H3.3. ATRX and DAXX have also evolved mutually-independent functions in transcription and chromatin dynamics. Here, using paediatric glioma and pancreatic neuroendocrine tumor cell lines, we identify a novel ATRX-independent function for DAXX in promoting genome stability by preventing transcription-associated R-loop accumulation and DNA double-strand break formation at centromeres. This function of DAXX required its interaction with histone H3.3 but was independent of H3.3 deposition and did not reflect a role in the repression of centromeric transcription. DAXX depletion mobilized BRCA1 at centromeres, in line with BRCA1 role in counteracting centromeric R-loop accumulation. Our results provide novel insights into the mechanisms protecting the human genome from chromosomal instability, as well as potential perspectives in the treatment of cancers with DAXX alterations.

Enzymatic activity of glycosyltransferase GLT8D1 promotes human glioblastoma cell migration.

Glioblastoma (GBM) is the most aggressive primary brain tumor characterized by infiltrative growth of malignant glioma cells into the surrounding brain parenchyma. In this study, our analysis of GBM patient cohorts revealed a significantly higher expression of Glycosyltransferase 8 domain containing 1 (GLT8D1) compared to normal brain tissue and could be associated with impaired patient survival. Increased in vitro expression of GLT8D1 significantly enhanced migration of two different sphere-forming GBM cell lines. By in silico analysis we predicted the 3D-structure as well as the active site residues of GLT8D1. The introduction of point mutations in the predicted active site reduced its glycosyltransferase activity in vitro and consequently impaired GBM tumor cell migration. Examination of GLT8D1 interaction partners by LC-MS/MS implied proteins associated with cytoskeleton and intracellular transport as potential substrates. In conclusion, we demonstrated that the enzymatic activity of glycosyltransferase GLT8D1 promotes GBM cell migration.

Fisetin protects against cardiac cell death through reduction of ROS production and caspases activity.

Myocardial infarction (MI) is a leading cause of death worldwide. Reperfusion is considered as an optimal therapy following cardiac ischemia. However, the promotion of a rapid elevation of O2 levels in ischemic cells produces high amounts of reactive oxygen species (ROS) leading to myocardial tissue injury. This phenomenon is called ischemia reperfusion injury (IRI). We aimed at identifying new and effective compounds to treat MI and minimize IRI. We previously studied heart regeneration following myocardial injury in zebrafish and described each step of the regeneration process, from the day of injury until complete recovery, in terms of transcriptional responses. Here, we mined the data and performed a deep in silico analysis to identify drugs highly likely to induce cardiac regeneration. Fisetin was identified as the top candidate. We validated its effects in an in vitro model of MI/IRI in mammalian cardiac cells. Fisetin enhances viability of rat cardiomyocytes following hypoxia/starvation - reoxygenation. It inhibits apoptosis, decreases ROS generation and caspase activation and protects from DNA damage. Interestingly, fisetin also activates genes involved in cell proliferation. Fisetin is thus a highly promising candidate drug with clinical potential to protect from ischemic damage following MI and to overcome IRI.

Hub genes in a pan-cancer co-expression network show potential for predicting drug responses.

Background: The topological analysis of networks extracted from different types of "omics" data is a useful strategy for characterizing biologically meaningful properties of the complex systems underlying these networks. In particular, the biological significance of highly connected genes in diverse molecular networks has been previously determined using data from several model organisms and phenotypes. Despite such insights, the predictive potential of candidate hubs in gene co-expression networks in the specific context of cancer-related drug experiments remains to be deeply investigated. The examination of such associations may offer opportunities for the accurate prediction of anticancer drug responses.  Methods: Here, we address this problem by: a) analyzing a co-expression network obtained from thousands of cancer cell lines, b) detecting significant network hubs, and c) assessing their capacity to predict drug sensitivity using data from thousands of drug experiments. We investigated the prediction capability of those genes using a multiple linear regression model, independent datasets, comparisons with other models and our own in vitro experiments. Results: These analyses led to the identification of 47 hub genes, which are implicated in a diverse range of cancer-relevant processes and pathways. Overall, encouraging agreements between predicted and observed drug sensitivities were observed in public datasets, as well as in our in vitro validations for four glioblastoma cell lines and four drugs. To facilitate further research, we share our hub-based drug sensitivity prediction model as an online tool. Conclusions: Our research shows that co-expression network hubs are biologically interesting and exhibit potential for predicting drug responses in vitro. These findings motivate further investigations about the relevance and application of our unbiased discovery approach in pre-clinical, translationally-oriented research.

Analysis of the dynamic co-expression network of heart regeneration in the zebrafish.

The zebrafish has the capacity to regenerate its heart after severe injury. While the function of a few genes during this process has been studied, we are far from fully understanding how genes interact to coordinate heart regeneration. To enable systematic insights into this phenomenon, we generated and integrated a dynamic co-expression network of heart regeneration in the zebrafish and linked systems-level properties to the underlying molecular events. Across multiple post-injury time points, the network displays topological attributes of biological relevance. We show that regeneration steps are mediated by modules of transcriptionally coordinated genes, and by genes acting as network hubs. We also established direct associations between hubs and validated drivers of heart regeneration with murine and human orthologs. The resulting models and interactive analysis tools are available at http://infused.vital-it.ch. Using a worked example, we demonstrate the usefulness of this unique open resource for hypothesis generation and in silico screening for genes involved in heart regeneration.

Transcriptional response to cardiac injury in the zebrafish: systematic identification of genes with highly concordant activity across in vivo models.

BACKGROUND: Zebrafish is a clinically-relevant model of heart regeneration. Unlike mammals, it has a remarkable heart repair capacity after injury, and promises novel translational applications. Amputation and cryoinjury models are key research tools for understanding injury response and regeneration in vivo. An understanding of the transcriptional responses following injury is needed to identify key players of heart tissue repair, as well as potential targets for boosting this property in humans. RESULTS: We investigated amputation and cryoinjury in vivo models of heart damage in the zebrafish through unbiased, integrative analyses of independent molecular datasets. To detect genes with potential biological roles, we derived computational prediction models with microarray data from heart amputation experiments. We focused on a top-ranked set of genes highly activated in the early post-injury stage, whose activity was further verified in independent microarray datasets. Next, we performed independent validations of expression responses with qPCR in a cryoinjury model. Across in vivo models, the top candidates showed highly concordant responses at 1 and 3 days post-injury, which highlights the predictive power of our analysis strategies and the possible biological relevance of these genes. Top candidates are significantly involved in cell fate specification and differentiation, and include heart failure markers such as periostin, as well as potential new targets for heart regeneration. For example, ptgis and ca2 were overexpressed, while usp2a, a regulator of the p53 pathway, was down-regulated in our in vivo models. Interestingly, a high activity of ptgis and ca2 has been previously observed in failing hearts from rats and humans. CONCLUSIONS: We identified genes with potential critical roles in the response to cardiac damage in the zebrafish. Their transcriptional activities are reproducible in different in vivo models of cardiac injury.

Analysis of a gene co-expression network establishes robust association between Col5a2 and ischemic heart disease.

BACKGROUND: This study aims to expand knowledge of the complex process of myocardial infarction (MI) through the application of a systems-based approach. METHODS: We generated a gene co-expression network from microarray data originating from a mouse model of MI. We characterized it on the basis of connectivity patterns and independent biological information. The potential clinical novelty and relevance of top predictions were assessed in the context of disease classification models. Models were validated using independent gene expression data from mouse and human samples. RESULTS: The gene co-expression network consisted of 178 genes and 7298 associations. The network was dissected into statistically and biologically meaningful communities of highly interconnected and co-expressed genes. Among the most significant communities, one was distinctly associated with molecular events underlying heart repair after MI (P < 0.05). Col5a2, a gene previously not specifically linked to MI response but responsible for the classic type of Ehlers-Danlos syndrome, was found to have many and strong co-expression associations within this community (11 connections with ρ > 0.85). To validate the potential clinical application of this discovery, we tested its disease discriminatory capacity on independently generated MI datasets from mice and humans. High classification accuracy and concordance was achieved across these evaluations with areas under the receiving operating characteristic curve above 0.8. CONCLUSION: Network-based approaches can enable the discovery of clinically-interesting predictive insights that are accurate and robust. Col5a2 shows predictive potential in MI, and in principle may represent a novel candidate marker for the identification and treatment of ischemic cardiovascular disease.

An optimized protocol for microarray validation by quantitative PCR using amplified amino allyl labeled RNA.

BACKGROUND: Validation of microarrays data by quantitative real-time PCR (qPCR) is often limited by the low amount of available RNA. This raised the possibility to perform validation experiments on the amplified amino allyl labeled RNA (AA-aRNA) leftover from microarrays. To test this possibility, we used an ongoing study of our laboratory aiming at identifying new biomarkers of graft rejection by the transcriptomic analysis of blood cells from brain-dead organ donors. RESULTS: qPCR for ACTB performed on AA-aRNA from 15 donors provided Cq values 8 cycles higher than when original RNA was used (P < 0.001), suggesting a strong inhibition of qPCR performed on AA-aRNA. When expression levels of 5 other genes were measured in AA-aRNA generated from a universal reference RNA, qPCR sensitivity and efficiency were decreased. This prevented the quantification of one low-abundant gene, which was readily quantified in un-amplified and un-labeled RNA. To overcome this limitation, we modified the reverse transcription (RT) protocol that generates cDNA from AA-aRNA as follows: addition of a denaturation step and 2-min incubation at room temperature to improve random primers annealing, a transcription initiation step to improve RT, and a final treatment with RNase H to degrade remaining RNA. Tested on universal reference AA-aRNA, these modifications provided a gain of 3.4 Cq (average from 5 genes, P < 0.001) and an increase of qPCR efficiency (from -1.96 to -2.88; P = 0.02). They also allowed for the detection of a low-abundant gene that was previously undetectable. Tested on AA-aRNA from 15 brain-dead organ donors, RT optimization provided a gain of 2.7 cycles (average from 7 genes, P = 0.004). Finally, qPCR results significantly correlated with microarrays. CONCLUSION: We present here an optimized RT protocol for validation of microarrays by qPCR from AA-aRNA. This is particularly valuable in experiments where limited amount of RNA is available.

Plasma storage at -80 degrees C does not protect matrix metalloproteinase-9 from degradation.

Recently, matrix metalloproteinase-9 (MMP-9) has been identified as a cardiovascular risk marker and is increasingly being determined in clinical studies. Among other matrix metalloproteinases, MMP-9 is known to be self-activable, as the cleavage of the propeptide leads to the formation of an active enzyme. In such a case the issue of storage of biological samples such as plasmas is of outstanding importance, as an enzymatic activity, although minimal, may remain at common storage temperature, i.e., -80 degrees C. Since 2000 our institute has created a plasma library from patients presenting with acute myocardial infarction. Recently, the evaluation of the MMP-9 led to the surprise of finding a dramatically low level of detectable enzyme in the oldest plasma samples. By using zymography, enzyme-linked immunosorbent assay and Western blots, we evaluated new and old samples and found that MMP-9 degrades over time. After 2 years, the detectable total MMP-9 dropped by 65%, and the asymptotic profile of the curve reached a residual 1% level after 43 months. These results were confirmed by zymography and western blotting. TIMP-1, the natural inhibitor of MMP-9 and MMP-2, remained rather stable over time. The results suggest that human plasma MMP-9 levels should be determined as soon as possible after sampling.