Experimental mitochondria-targeted DNA methylation identifies GpC methylation, not CpG methylation, as potential regulator of mitochondrial gene expression

Wijst, Monique G. P. van der; Tilburg, Amanda Y van; Ruiters, Marcel H. J.; Rots, Marianne G.

doi:10.1038/s41598-017-00263-z

Cited by 74 publications

(78 citation statements)

References 62 publications

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“…Mitochondrial genes are located close to each other (37 genes on 16.5kb). The mtDNA has no introns, and only a small noncoding region . As all protein‐encoding mitochondrial genes encode proteins involved in oxidative phosphorylation, all diseases caused by mutations in the mitochondrial genome are characterized by defective oxidative phosphorylation.…”

Section: Discussionmentioning

confidence: 99%

“…The mtDNA has no introns, and only a small noncoding region. 6 As all protein-encoding mitochondrial genes encode proteins involved in oxidative phosphorylation, all diseases caused by mutations in the mitochondrial genome are characterized by defective oxidative phosphorylation. In most cases, this leads to multisystem disorders, but some mitochondrial diseases can cause dysfunction in one organ, often the cochlea.…”

Section: Discussionmentioning

confidence: 99%

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Temporal bone histopathology in MELAS syndrome

Handzel

Ungar

Lee

et al. 2020

Laryngoscope Investig Oto

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Objectives Describe the histopathology of the temporal bones in MELAS (myopathy, encephalopathy, lactic acidosis, and stroke‐like episodes) syndrome. The syndrome results from a known point mutation in mitochondrial DNA. Methods Histopathology analysis of a pair of temporal bones from the oldest surviving MELAS syndrome temporal bone donor. Histopathologic findings were correlated with known premortem clinical data. Results The inner ears showed severe but incomplete atrophy of the stria vascularis for the length of the cochleae. In contrast, the organ of Corti and inner hair cells appeared intact with some loss of outer hair cells. Other than moderate loss at the basal turn, spiral ganglion cells numbers were normal. The vestibular neuroepithelium was mostly normal with the exception of moderate degeneration of the macula sacculi and partial collapse of the saccular wall on the right. The cerebral cortex had infarct‐like lesions with adjacent gliosis. Conclusion This is an analysis of the oldest patient with MELAS syndrome to date, an addition to only two previously published patients. It supports the notion that hearing loss is a result of dysfunction of the stria vascularis and not loss of hair cells or neurons. Patterns of vestibular pathology are in agreement to in‐vivo measurements. These findings support auditory rehabilitation with cochlear implants and may be relevant to hearing loss due to other mitochondrial mutations. Level of evidence 4

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Temporal bone histopathology in MELAS syndrome

Handzel

Ungar

Lee

et al. 2020

Laryngoscope Investig Oto

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“…Since mitochondria are dominant organelles regarding intracellular energy and because they have their own independent DNA genome, it is important to look at the genomic alterations of this organelle in certain diseases. These alterations are associated particularly with various pulmonary diseases and lung cancers [57,58].…”

Section: Mitochondrial Methylationmentioning

confidence: 99%

Epigenetic Modifications and Potential Treatment Approaches in Lung Cancers

Budak¹,

Yıldız²

2018

Lung Cancer - Strategies for Diagnosis and Treatment

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Alteration of methylation is a process seen across a wide variety of species ranging from bacterial microorganisms to mammals, defined as the adaptation method the organism develops against environmental or intrinsic effects, or employs to switch off the genome regions which are no longer required through the evolutionary process. Scientific advancements have allowed detecting the regions that undergo different patterns of methylation. It has been demonstrated that the control on changes in gene expression is not guided by transcription factors alone and that epigenetic alterations are also involved in this process. Furthermore, epigenetic modifications have been shown to be considerably important in cancer development. This section focuses on epigenetic changes and potential treatment options in lung cancer.

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“…This process has therefore been extensively observed and studied in the context of cancer [24], [15], [41], [29]. While various forms of cancer are central to the discussion on DNA methylation, it has also been linked to other diseases and biological processes such as cardiovascular disease [13] and Alzheimer's Disease [19] as well as gene expression regulation in general [40] and epigenetic editing [35]. Hence, researchers have long been measuring DNA methylation levels.…”

Section: Introductionmentioning

confidence: 99%

Predicting Methylation from Sequence and Gene Expression Using Deep Learning with Attention

Levy-Jurgenson

Tekpli

Kristensen

et al. 2018

Preprint

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DNA methylation has been extensively linked to alterations in gene expression, playing a key role in the manifestation of multiple diseases, most notably cancer. For this reason, researchers have long been measuring DNA methylation in living organisms. The relationship between methylation and expression, and between methylation in different genomic regions is of great theoretical interest from a molecular biology perspective. Therefore, several models have been suggested to support the prediction of methylation status in samples. These models, however, have two main limitations: (a) they heavily rely on partially measured methylation levels as input, somewhat defeating the object as one is required to collect measurements from the sample of interest before applying the model; and (b) they are largely based on human mediated feature engineering, thus preventing the model from unveiling its own representations. To address these limitations we used deep learning, with an attention mechanism, to produce a general model that predicts DNA methylation for a given sample in any CpG position based solely on the sample's gene expression profile and the sequence surrounding the CpG.We show that our model is capable of generalizing to a completely separate test set of CpG positions and subjects. Depending on gene-CpG proximity conditions, our model can attain a Spearman correlation of up to 0.8 and MAE of 0.14 for thousands of CpG sites in the test data. We also identify and analyze several motifs and genes that our model suggests may be linked to methylation activity, such as Nodal and Hand1. Moreover, our approach, and most notably the use of attention mechanisms, offers a novel framework with which to extract valuable insights from gene expression data when combined with sequence information. The code and trained models are available at: https://github.com/YakhiniGroup/Methylation

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Experimental mitochondria-targeted DNA methylation identifies GpC methylation, not CpG methylation, as potential regulator of mitochondrial gene expression

Cited by 74 publications

References 62 publications

Temporal bone histopathology in MELAS syndrome

Temporal bone histopathology in MELAS syndrome

Epigenetic Modifications and Potential Treatment Approaches in Lung Cancers

Predicting Methylation from Sequence and Gene Expression Using Deep Learning with Attention

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